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<|file_name|>dialogue.py<|end_file_name|><｜fim▁begin｜>import numpy as np
from numpy import cumsum, sum, searchsorted
from numpy.random import rand
import math
import utils
import core.sentence as sentence
import core.markovchain as mc
import logging
logger = logging.getLogger(__name__)
# Dialogue making class. Need to review where to return a string, where to return a list of tokens, etc.
# setters: list of speakers, pronouns, priors etc.
# random transitions
# Internal: build list of structures:
# e.g.{:speaker_name "Alice", :speaker_pronoun "she", :speaker_str "she", :speech_verb "said", :position "end"}
# Then end with fn that maps that out to a suitable string
# e.g. "<SPEECH>, she said."
# External bit then replaces <SPEECH> with a markov-chain-generated sentence (or several).
class dialogue_maker(object):
"""Class to handle creating dialogue based on a list of speakers and a sentence generator."""
def __init__(self, names, pronouns, mc):
self.speakers = [{"name": n, "pronoun": p} for n, p in list(zip(names, pronouns))]
self._transitions = self.make_transition_probs()
self._speech_acts = ["said", "whispered", "shouted", "cried"]
self._acts_transitions = [25, 2, 2, 2]
self.mc = mc
# self.seeds = seeds
self.target_len = np.random.randint(5, 50, size=len(names)) # rough words per sentence
def make_transition_probs(self):
"""Make transition matrix between speakers, with random symmetric biases added in"""
n = len(self.speakers) # TODO why this line ???
transitions = np.random.randint(5, size=(n, n)) + 1
transitions += transitions.transpose()
for i in range(0, math.floor(n / 2)):
s1 = np.random.randint(n)
s2 = np.random.randint(n)
transitions[s1][s2] += 10
transitions[s2][s1] += 8
return(transitions)
def after(self, speaker_id):
"""Pick next person to speak"""
row = self._transitions[speaker_id]
sucessor = searchsorted(cumsum(row), rand() * sum(row))
return sucessor
def speaker_sequence(self, speaker_id, n):
"""Random walk through transitions matrix to produce a sequence of speaker ids"""
seq = []
for i in range(n):
seq.append(speaker_id)
speaker_id = self.after(speaker_id)
return seq
def speech_sequence(self, n):
speech_acts_seq = []
next_speech_id = 0
for i in range(n):
next_speech_id = searchsorted(cumsum(self._acts_transitions), rand() * sum(self._acts_transitions))
speech_acts_seq.append(self._speech_acts[next_speech_id])
return speech_acts_seq
def seq_to_names(self, sequence):
return([self.speakers[id] for id in sequence])
def make_speech_bits(self, seeds):
n = len(seeds)
speaker_id = self.speaker_sequence(0, n)
speech_acts_seq = self.speech_sequence(n)
bits = []
ss = sentence.SentenceMaker(self.mc)
for i in range(n):
sent_toks = ss.generate_sentence_tokens([seeds[i]], self.target_len[speaker_id[i]])
sent_toks = ss.polish_sentence(sent_toks)
bits.append({'speaker_name': self.speakers[speaker_id[i]]["name"],
'speech_act': speech_acts_seq[speaker_id[i]],
'seq_id': speaker_id[i],
'speech': sent_toks,
'paragraph': True})
return(bits)
def simplify(self, seq_map):
"Take a sequence of speech parts and make more natural by removing name reptition etc."
for i in range(0, len(seq_map)):
seq_map[i]['speaker_str'] = seq_map[i]['speaker_name'] # default
# Same speaker contiues:
if i > 0 and seq_map[i]['seq_id'] == seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = False
else:
if i > 1 and seq_map[i]['seq_id'] == seq_map[i - 2]['seq_id'] \
and seq_map[i]['seq_id'] != seq_map[i - 1]['seq_id']:
<|fim_middle|>
return seq_map
def report_seq(self, seq_map):
"""Convert sequence of speeches to a tokens."""
sents = []
for i in range(0, len(seq_map)):
if seq_map[i]['paragraph']:
# text += "\n "
quote_start = '"'
else:
quote_start = ""
if i > len(seq_map) - 2 or seq_map[i + 1]['paragraph']:
quote_end = '"'
else:
quote_end = " "
if len(seq_map[i]['speech_act']) > 0:
speech_act = seq_map[i]['speech_act'] + ","
else:
speech_act = seq_map[i]['speech_act']
tokens = [utils.START_TOKEN]
tokens.append(seq_map[i]['speaker_str'])
tokens.append(speech_act)
tokens.append(quote_start)
tokens.extend(seq_map[i]['speech'][1:-1])
tokens.append(quote_end)
tokens.append(utils.END_TOKEN)
sents.append(tokens)
return sents
def make_dialogue(self, seeds):
"""Returns a list of sentences, each being a list of tokens."""
acts = self.make_speech_bits(seeds)
seq_map = self.simplify(acts)
sents = self.report_seq(seq_map)
return(sents)
def dev():
import knowledge.names as names
mcW = mc.MarkovChain()
nm = names.NameMaker()
speakers = [nm.random_person() for i in range(1, 4)]
dm = dialogue_maker([n['name'] for n in speakers], [n['pronoun'] for n in speakers], mcW)
dlg = dm.make_dialogue(["dog", "run", "spot"])
print(dlg)
<｜fim▁end｜> | seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = True |
<|file_name|>dialogue.py<|end_file_name|><｜fim▁begin｜>import numpy as np
from numpy import cumsum, sum, searchsorted
from numpy.random import rand
import math
import utils
import core.sentence as sentence
import core.markovchain as mc
import logging
logger = logging.getLogger(__name__)
# Dialogue making class. Need to review where to return a string, where to return a list of tokens, etc.
# setters: list of speakers, pronouns, priors etc.
# random transitions
# Internal: build list of structures:
# e.g.{:speaker_name "Alice", :speaker_pronoun "she", :speaker_str "she", :speech_verb "said", :position "end"}
# Then end with fn that maps that out to a suitable string
# e.g. "<SPEECH>, she said."
# External bit then replaces <SPEECH> with a markov-chain-generated sentence (or several).
class dialogue_maker(object):
"""Class to handle creating dialogue based on a list of speakers and a sentence generator."""
def __init__(self, names, pronouns, mc):
self.speakers = [{"name": n, "pronoun": p} for n, p in list(zip(names, pronouns))]
self._transitions = self.make_transition_probs()
self._speech_acts = ["said", "whispered", "shouted", "cried"]
self._acts_transitions = [25, 2, 2, 2]
self.mc = mc
# self.seeds = seeds
self.target_len = np.random.randint(5, 50, size=len(names)) # rough words per sentence
def make_transition_probs(self):
"""Make transition matrix between speakers, with random symmetric biases added in"""
n = len(self.speakers) # TODO why this line ???
transitions = np.random.randint(5, size=(n, n)) + 1
transitions += transitions.transpose()
for i in range(0, math.floor(n / 2)):
s1 = np.random.randint(n)
s2 = np.random.randint(n)
transitions[s1][s2] += 10
transitions[s2][s1] += 8
return(transitions)
def after(self, speaker_id):
"""Pick next person to speak"""
row = self._transitions[speaker_id]
sucessor = searchsorted(cumsum(row), rand() * sum(row))
return sucessor
def speaker_sequence(self, speaker_id, n):
"""Random walk through transitions matrix to produce a sequence of speaker ids"""
seq = []
for i in range(n):
seq.append(speaker_id)
speaker_id = self.after(speaker_id)
return seq
def speech_sequence(self, n):
speech_acts_seq = []
next_speech_id = 0
for i in range(n):
next_speech_id = searchsorted(cumsum(self._acts_transitions), rand() * sum(self._acts_transitions))
speech_acts_seq.append(self._speech_acts[next_speech_id])
return speech_acts_seq
def seq_to_names(self, sequence):
return([self.speakers[id] for id in sequence])
def make_speech_bits(self, seeds):
n = len(seeds)
speaker_id = self.speaker_sequence(0, n)
speech_acts_seq = self.speech_sequence(n)
bits = []
ss = sentence.SentenceMaker(self.mc)
for i in range(n):
sent_toks = ss.generate_sentence_tokens([seeds[i]], self.target_len[speaker_id[i]])
sent_toks = ss.polish_sentence(sent_toks)
bits.append({'speaker_name': self.speakers[speaker_id[i]]["name"],
'speech_act': speech_acts_seq[speaker_id[i]],
'seq_id': speaker_id[i],
'speech': sent_toks,
'paragraph': True})
return(bits)
def simplify(self, seq_map):
"Take a sequence of speech parts and make more natural by removing name reptition etc."
for i in range(0, len(seq_map)):
seq_map[i]['speaker_str'] = seq_map[i]['speaker_name'] # default
# Same speaker contiues:
if i > 0 and seq_map[i]['seq_id'] == seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = False
else:
if i > 1 and seq_map[i]['seq_id'] == seq_map[i - 2]['seq_id'] \
and seq_map[i]['seq_id'] != seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = True
return seq_map
def report_seq(self, seq_map):
"""Convert sequence of speeches to a tokens."""
sents = []
for i in range(0, len(seq_map)):
if seq_map[i]['paragraph']:
# text += "\n "
<|fim_middle|>
else:
quote_start = ""
if i > len(seq_map) - 2 or seq_map[i + 1]['paragraph']:
quote_end = '"'
else:
quote_end = " "
if len(seq_map[i]['speech_act']) > 0:
speech_act = seq_map[i]['speech_act'] + ","
else:
speech_act = seq_map[i]['speech_act']
tokens = [utils.START_TOKEN]
tokens.append(seq_map[i]['speaker_str'])
tokens.append(speech_act)
tokens.append(quote_start)
tokens.extend(seq_map[i]['speech'][1:-1])
tokens.append(quote_end)
tokens.append(utils.END_TOKEN)
sents.append(tokens)
return sents
def make_dialogue(self, seeds):
"""Returns a list of sentences, each being a list of tokens."""
acts = self.make_speech_bits(seeds)
seq_map = self.simplify(acts)
sents = self.report_seq(seq_map)
return(sents)
def dev():
import knowledge.names as names
mcW = mc.MarkovChain()
nm = names.NameMaker()
speakers = [nm.random_person() for i in range(1, 4)]
dm = dialogue_maker([n['name'] for n in speakers], [n['pronoun'] for n in speakers], mcW)
dlg = dm.make_dialogue(["dog", "run", "spot"])
print(dlg)
<｜fim▁end｜> | quote_start = '"' |
<|file_name|>dialogue.py<|end_file_name|><｜fim▁begin｜>import numpy as np
from numpy import cumsum, sum, searchsorted
from numpy.random import rand
import math
import utils
import core.sentence as sentence
import core.markovchain as mc
import logging
logger = logging.getLogger(__name__)
# Dialogue making class. Need to review where to return a string, where to return a list of tokens, etc.
# setters: list of speakers, pronouns, priors etc.
# random transitions
# Internal: build list of structures:
# e.g.{:speaker_name "Alice", :speaker_pronoun "she", :speaker_str "she", :speech_verb "said", :position "end"}
# Then end with fn that maps that out to a suitable string
# e.g. "<SPEECH>, she said."
# External bit then replaces <SPEECH> with a markov-chain-generated sentence (or several).
class dialogue_maker(object):
"""Class to handle creating dialogue based on a list of speakers and a sentence generator."""
def __init__(self, names, pronouns, mc):
self.speakers = [{"name": n, "pronoun": p} for n, p in list(zip(names, pronouns))]
self._transitions = self.make_transition_probs()
self._speech_acts = ["said", "whispered", "shouted", "cried"]
self._acts_transitions = [25, 2, 2, 2]
self.mc = mc
# self.seeds = seeds
self.target_len = np.random.randint(5, 50, size=len(names)) # rough words per sentence
def make_transition_probs(self):
"""Make transition matrix between speakers, with random symmetric biases added in"""
n = len(self.speakers) # TODO why this line ???
transitions = np.random.randint(5, size=(n, n)) + 1
transitions += transitions.transpose()
for i in range(0, math.floor(n / 2)):
s1 = np.random.randint(n)
s2 = np.random.randint(n)
transitions[s1][s2] += 10
transitions[s2][s1] += 8
return(transitions)
def after(self, speaker_id):
"""Pick next person to speak"""
row = self._transitions[speaker_id]
sucessor = searchsorted(cumsum(row), rand() * sum(row))
return sucessor
def speaker_sequence(self, speaker_id, n):
"""Random walk through transitions matrix to produce a sequence of speaker ids"""
seq = []
for i in range(n):
seq.append(speaker_id)
speaker_id = self.after(speaker_id)
return seq
def speech_sequence(self, n):
speech_acts_seq = []
next_speech_id = 0
for i in range(n):
next_speech_id = searchsorted(cumsum(self._acts_transitions), rand() * sum(self._acts_transitions))
speech_acts_seq.append(self._speech_acts[next_speech_id])
return speech_acts_seq
def seq_to_names(self, sequence):
return([self.speakers[id] for id in sequence])
def make_speech_bits(self, seeds):
n = len(seeds)
speaker_id = self.speaker_sequence(0, n)
speech_acts_seq = self.speech_sequence(n)
bits = []
ss = sentence.SentenceMaker(self.mc)
for i in range(n):
sent_toks = ss.generate_sentence_tokens([seeds[i]], self.target_len[speaker_id[i]])
sent_toks = ss.polish_sentence(sent_toks)
bits.append({'speaker_name': self.speakers[speaker_id[i]]["name"],
'speech_act': speech_acts_seq[speaker_id[i]],
'seq_id': speaker_id[i],
'speech': sent_toks,
'paragraph': True})
return(bits)
def simplify(self, seq_map):
"Take a sequence of speech parts and make more natural by removing name reptition etc."
for i in range(0, len(seq_map)):
seq_map[i]['speaker_str'] = seq_map[i]['speaker_name'] # default
# Same speaker contiues:
if i > 0 and seq_map[i]['seq_id'] == seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = False
else:
if i > 1 and seq_map[i]['seq_id'] == seq_map[i - 2]['seq_id'] \
and seq_map[i]['seq_id'] != seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = True
return seq_map
def report_seq(self, seq_map):
"""Convert sequence of speeches to a tokens."""
sents = []
for i in range(0, len(seq_map)):
if seq_map[i]['paragraph']:
# text += "\n "
quote_start = '"'
else:
<|fim_middle|>
if i > len(seq_map) - 2 or seq_map[i + 1]['paragraph']:
quote_end = '"'
else:
quote_end = " "
if len(seq_map[i]['speech_act']) > 0:
speech_act = seq_map[i]['speech_act'] + ","
else:
speech_act = seq_map[i]['speech_act']
tokens = [utils.START_TOKEN]
tokens.append(seq_map[i]['speaker_str'])
tokens.append(speech_act)
tokens.append(quote_start)
tokens.extend(seq_map[i]['speech'][1:-1])
tokens.append(quote_end)
tokens.append(utils.END_TOKEN)
sents.append(tokens)
return sents
def make_dialogue(self, seeds):
"""Returns a list of sentences, each being a list of tokens."""
acts = self.make_speech_bits(seeds)
seq_map = self.simplify(acts)
sents = self.report_seq(seq_map)
return(sents)
def dev():
import knowledge.names as names
mcW = mc.MarkovChain()
nm = names.NameMaker()
speakers = [nm.random_person() for i in range(1, 4)]
dm = dialogue_maker([n['name'] for n in speakers], [n['pronoun'] for n in speakers], mcW)
dlg = dm.make_dialogue(["dog", "run", "spot"])
print(dlg)
<｜fim▁end｜> | quote_start = "" |
<|file_name|>dialogue.py<|end_file_name|><｜fim▁begin｜>import numpy as np
from numpy import cumsum, sum, searchsorted
from numpy.random import rand
import math
import utils
import core.sentence as sentence
import core.markovchain as mc
import logging
logger = logging.getLogger(__name__)
# Dialogue making class. Need to review where to return a string, where to return a list of tokens, etc.
# setters: list of speakers, pronouns, priors etc.
# random transitions
# Internal: build list of structures:
# e.g.{:speaker_name "Alice", :speaker_pronoun "she", :speaker_str "she", :speech_verb "said", :position "end"}
# Then end with fn that maps that out to a suitable string
# e.g. "<SPEECH>, she said."
# External bit then replaces <SPEECH> with a markov-chain-generated sentence (or several).
class dialogue_maker(object):
"""Class to handle creating dialogue based on a list of speakers and a sentence generator."""
def __init__(self, names, pronouns, mc):
self.speakers = [{"name": n, "pronoun": p} for n, p in list(zip(names, pronouns))]
self._transitions = self.make_transition_probs()
self._speech_acts = ["said", "whispered", "shouted", "cried"]
self._acts_transitions = [25, 2, 2, 2]
self.mc = mc
# self.seeds = seeds
self.target_len = np.random.randint(5, 50, size=len(names)) # rough words per sentence
def make_transition_probs(self):
"""Make transition matrix between speakers, with random symmetric biases added in"""
n = len(self.speakers) # TODO why this line ???
transitions = np.random.randint(5, size=(n, n)) + 1
transitions += transitions.transpose()
for i in range(0, math.floor(n / 2)):
s1 = np.random.randint(n)
s2 = np.random.randint(n)
transitions[s1][s2] += 10
transitions[s2][s1] += 8
return(transitions)
def after(self, speaker_id):
"""Pick next person to speak"""
row = self._transitions[speaker_id]
sucessor = searchsorted(cumsum(row), rand() * sum(row))
return sucessor
def speaker_sequence(self, speaker_id, n):
"""Random walk through transitions matrix to produce a sequence of speaker ids"""
seq = []
for i in range(n):
seq.append(speaker_id)
speaker_id = self.after(speaker_id)
return seq
def speech_sequence(self, n):
speech_acts_seq = []
next_speech_id = 0
for i in range(n):
next_speech_id = searchsorted(cumsum(self._acts_transitions), rand() * sum(self._acts_transitions))
speech_acts_seq.append(self._speech_acts[next_speech_id])
return speech_acts_seq
def seq_to_names(self, sequence):
return([self.speakers[id] for id in sequence])
def make_speech_bits(self, seeds):
n = len(seeds)
speaker_id = self.speaker_sequence(0, n)
speech_acts_seq = self.speech_sequence(n)
bits = []
ss = sentence.SentenceMaker(self.mc)
for i in range(n):
sent_toks = ss.generate_sentence_tokens([seeds[i]], self.target_len[speaker_id[i]])
sent_toks = ss.polish_sentence(sent_toks)
bits.append({'speaker_name': self.speakers[speaker_id[i]]["name"],
'speech_act': speech_acts_seq[speaker_id[i]],
'seq_id': speaker_id[i],
'speech': sent_toks,
'paragraph': True})
return(bits)
def simplify(self, seq_map):
"Take a sequence of speech parts and make more natural by removing name reptition etc."
for i in range(0, len(seq_map)):
seq_map[i]['speaker_str'] = seq_map[i]['speaker_name'] # default
# Same speaker contiues:
if i > 0 and seq_map[i]['seq_id'] == seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = False
else:
if i > 1 and seq_map[i]['seq_id'] == seq_map[i - 2]['seq_id'] \
and seq_map[i]['seq_id'] != seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = True
return seq_map
def report_seq(self, seq_map):
"""Convert sequence of speeches to a tokens."""
sents = []
for i in range(0, len(seq_map)):
if seq_map[i]['paragraph']:
# text += "\n "
quote_start = '"'
else:
quote_start = ""
if i > len(seq_map) - 2 or seq_map[i + 1]['paragraph']:
<|fim_middle|>
else:
quote_end = " "
if len(seq_map[i]['speech_act']) > 0:
speech_act = seq_map[i]['speech_act'] + ","
else:
speech_act = seq_map[i]['speech_act']
tokens = [utils.START_TOKEN]
tokens.append(seq_map[i]['speaker_str'])
tokens.append(speech_act)
tokens.append(quote_start)
tokens.extend(seq_map[i]['speech'][1:-1])
tokens.append(quote_end)
tokens.append(utils.END_TOKEN)
sents.append(tokens)
return sents
def make_dialogue(self, seeds):
"""Returns a list of sentences, each being a list of tokens."""
acts = self.make_speech_bits(seeds)
seq_map = self.simplify(acts)
sents = self.report_seq(seq_map)
return(sents)
def dev():
import knowledge.names as names
mcW = mc.MarkovChain()
nm = names.NameMaker()
speakers = [nm.random_person() for i in range(1, 4)]
dm = dialogue_maker([n['name'] for n in speakers], [n['pronoun'] for n in speakers], mcW)
dlg = dm.make_dialogue(["dog", "run", "spot"])
print(dlg)
<｜fim▁end｜> | quote_end = '"' |
<|file_name|>dialogue.py<|end_file_name|><｜fim▁begin｜>import numpy as np
from numpy import cumsum, sum, searchsorted
from numpy.random import rand
import math
import utils
import core.sentence as sentence
import core.markovchain as mc
import logging
logger = logging.getLogger(__name__)
# Dialogue making class. Need to review where to return a string, where to return a list of tokens, etc.
# setters: list of speakers, pronouns, priors etc.
# random transitions
# Internal: build list of structures:
# e.g.{:speaker_name "Alice", :speaker_pronoun "she", :speaker_str "she", :speech_verb "said", :position "end"}
# Then end with fn that maps that out to a suitable string
# e.g. "<SPEECH>, she said."
# External bit then replaces <SPEECH> with a markov-chain-generated sentence (or several).
class dialogue_maker(object):
"""Class to handle creating dialogue based on a list of speakers and a sentence generator."""
def __init__(self, names, pronouns, mc):
self.speakers = [{"name": n, "pronoun": p} for n, p in list(zip(names, pronouns))]
self._transitions = self.make_transition_probs()
self._speech_acts = ["said", "whispered", "shouted", "cried"]
self._acts_transitions = [25, 2, 2, 2]
self.mc = mc
# self.seeds = seeds
self.target_len = np.random.randint(5, 50, size=len(names)) # rough words per sentence
def make_transition_probs(self):
"""Make transition matrix between speakers, with random symmetric biases added in"""
n = len(self.speakers) # TODO why this line ???
transitions = np.random.randint(5, size=(n, n)) + 1
transitions += transitions.transpose()
for i in range(0, math.floor(n / 2)):
s1 = np.random.randint(n)
s2 = np.random.randint(n)
transitions[s1][s2] += 10
transitions[s2][s1] += 8
return(transitions)
def after(self, speaker_id):
"""Pick next person to speak"""
row = self._transitions[speaker_id]
sucessor = searchsorted(cumsum(row), rand() * sum(row))
return sucessor
def speaker_sequence(self, speaker_id, n):
"""Random walk through transitions matrix to produce a sequence of speaker ids"""
seq = []
for i in range(n):
seq.append(speaker_id)
speaker_id = self.after(speaker_id)
return seq
def speech_sequence(self, n):
speech_acts_seq = []
next_speech_id = 0
for i in range(n):
next_speech_id = searchsorted(cumsum(self._acts_transitions), rand() * sum(self._acts_transitions))
speech_acts_seq.append(self._speech_acts[next_speech_id])
return speech_acts_seq
def seq_to_names(self, sequence):
return([self.speakers[id] for id in sequence])
def make_speech_bits(self, seeds):
n = len(seeds)
speaker_id = self.speaker_sequence(0, n)
speech_acts_seq = self.speech_sequence(n)
bits = []
ss = sentence.SentenceMaker(self.mc)
for i in range(n):
sent_toks = ss.generate_sentence_tokens([seeds[i]], self.target_len[speaker_id[i]])
sent_toks = ss.polish_sentence(sent_toks)
bits.append({'speaker_name': self.speakers[speaker_id[i]]["name"],
'speech_act': speech_acts_seq[speaker_id[i]],
'seq_id': speaker_id[i],
'speech': sent_toks,
'paragraph': True})
return(bits)
def simplify(self, seq_map):
"Take a sequence of speech parts and make more natural by removing name reptition etc."
for i in range(0, len(seq_map)):
seq_map[i]['speaker_str'] = seq_map[i]['speaker_name'] # default
# Same speaker contiues:
if i > 0 and seq_map[i]['seq_id'] == seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = False
else:
if i > 1 and seq_map[i]['seq_id'] == seq_map[i - 2]['seq_id'] \
and seq_map[i]['seq_id'] != seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = True
return seq_map
def report_seq(self, seq_map):
"""Convert sequence of speeches to a tokens."""
sents = []
for i in range(0, len(seq_map)):
if seq_map[i]['paragraph']:
# text += "\n "
quote_start = '"'
else:
quote_start = ""
if i > len(seq_map) - 2 or seq_map[i + 1]['paragraph']:
quote_end = '"'
else:
<|fim_middle|>
if len(seq_map[i]['speech_act']) > 0:
speech_act = seq_map[i]['speech_act'] + ","
else:
speech_act = seq_map[i]['speech_act']
tokens = [utils.START_TOKEN]
tokens.append(seq_map[i]['speaker_str'])
tokens.append(speech_act)
tokens.append(quote_start)
tokens.extend(seq_map[i]['speech'][1:-1])
tokens.append(quote_end)
tokens.append(utils.END_TOKEN)
sents.append(tokens)
return sents
def make_dialogue(self, seeds):
"""Returns a list of sentences, each being a list of tokens."""
acts = self.make_speech_bits(seeds)
seq_map = self.simplify(acts)
sents = self.report_seq(seq_map)
return(sents)
def dev():
import knowledge.names as names
mcW = mc.MarkovChain()
nm = names.NameMaker()
speakers = [nm.random_person() for i in range(1, 4)]
dm = dialogue_maker([n['name'] for n in speakers], [n['pronoun'] for n in speakers], mcW)
dlg = dm.make_dialogue(["dog", "run", "spot"])
print(dlg)
<｜fim▁end｜> | quote_end = " " |
<|file_name|>dialogue.py<|end_file_name|><｜fim▁begin｜>import numpy as np
from numpy import cumsum, sum, searchsorted
from numpy.random import rand
import math
import utils
import core.sentence as sentence
import core.markovchain as mc
import logging
logger = logging.getLogger(__name__)
# Dialogue making class. Need to review where to return a string, where to return a list of tokens, etc.
# setters: list of speakers, pronouns, priors etc.
# random transitions
# Internal: build list of structures:
# e.g.{:speaker_name "Alice", :speaker_pronoun "she", :speaker_str "she", :speech_verb "said", :position "end"}
# Then end with fn that maps that out to a suitable string
# e.g. "<SPEECH>, she said."
# External bit then replaces <SPEECH> with a markov-chain-generated sentence (or several).
class dialogue_maker(object):
"""Class to handle creating dialogue based on a list of speakers and a sentence generator."""
def __init__(self, names, pronouns, mc):
self.speakers = [{"name": n, "pronoun": p} for n, p in list(zip(names, pronouns))]
self._transitions = self.make_transition_probs()
self._speech_acts = ["said", "whispered", "shouted", "cried"]
self._acts_transitions = [25, 2, 2, 2]
self.mc = mc
# self.seeds = seeds
self.target_len = np.random.randint(5, 50, size=len(names)) # rough words per sentence
def make_transition_probs(self):
"""Make transition matrix between speakers, with random symmetric biases added in"""
n = len(self.speakers) # TODO why this line ???
transitions = np.random.randint(5, size=(n, n)) + 1
transitions += transitions.transpose()
for i in range(0, math.floor(n / 2)):
s1 = np.random.randint(n)
s2 = np.random.randint(n)
transitions[s1][s2] += 10
transitions[s2][s1] += 8
return(transitions)
def after(self, speaker_id):
"""Pick next person to speak"""
row = self._transitions[speaker_id]
sucessor = searchsorted(cumsum(row), rand() * sum(row))
return sucessor
def speaker_sequence(self, speaker_id, n):
"""Random walk through transitions matrix to produce a sequence of speaker ids"""
seq = []
for i in range(n):
seq.append(speaker_id)
speaker_id = self.after(speaker_id)
return seq
def speech_sequence(self, n):
speech_acts_seq = []
next_speech_id = 0
for i in range(n):
next_speech_id = searchsorted(cumsum(self._acts_transitions), rand() * sum(self._acts_transitions))
speech_acts_seq.append(self._speech_acts[next_speech_id])
return speech_acts_seq
def seq_to_names(self, sequence):
return([self.speakers[id] for id in sequence])
def make_speech_bits(self, seeds):
n = len(seeds)
speaker_id = self.speaker_sequence(0, n)
speech_acts_seq = self.speech_sequence(n)
bits = []
ss = sentence.SentenceMaker(self.mc)
for i in range(n):
sent_toks = ss.generate_sentence_tokens([seeds[i]], self.target_len[speaker_id[i]])
sent_toks = ss.polish_sentence(sent_toks)
bits.append({'speaker_name': self.speakers[speaker_id[i]]["name"],
'speech_act': speech_acts_seq[speaker_id[i]],
'seq_id': speaker_id[i],
'speech': sent_toks,
'paragraph': True})
return(bits)
def simplify(self, seq_map):
"Take a sequence of speech parts and make more natural by removing name reptition etc."
for i in range(0, len(seq_map)):
seq_map[i]['speaker_str'] = seq_map[i]['speaker_name'] # default
# Same speaker contiues:
if i > 0 and seq_map[i]['seq_id'] == seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = False
else:
if i > 1 and seq_map[i]['seq_id'] == seq_map[i - 2]['seq_id'] \
and seq_map[i]['seq_id'] != seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = True
return seq_map
def report_seq(self, seq_map):
"""Convert sequence of speeches to a tokens."""
sents = []
for i in range(0, len(seq_map)):
if seq_map[i]['paragraph']:
# text += "\n "
quote_start = '"'
else:
quote_start = ""
if i > len(seq_map) - 2 or seq_map[i + 1]['paragraph']:
quote_end = '"'
else:
quote_end = " "
if len(seq_map[i]['speech_act']) > 0:
<|fim_middle|>
else:
speech_act = seq_map[i]['speech_act']
tokens = [utils.START_TOKEN]
tokens.append(seq_map[i]['speaker_str'])
tokens.append(speech_act)
tokens.append(quote_start)
tokens.extend(seq_map[i]['speech'][1:-1])
tokens.append(quote_end)
tokens.append(utils.END_TOKEN)
sents.append(tokens)
return sents
def make_dialogue(self, seeds):
"""Returns a list of sentences, each being a list of tokens."""
acts = self.make_speech_bits(seeds)
seq_map = self.simplify(acts)
sents = self.report_seq(seq_map)
return(sents)
def dev():
import knowledge.names as names
mcW = mc.MarkovChain()
nm = names.NameMaker()
speakers = [nm.random_person() for i in range(1, 4)]
dm = dialogue_maker([n['name'] for n in speakers], [n['pronoun'] for n in speakers], mcW)
dlg = dm.make_dialogue(["dog", "run", "spot"])
print(dlg)
<｜fim▁end｜> | speech_act = seq_map[i]['speech_act'] + "," |
<|file_name|>dialogue.py<|end_file_name|><｜fim▁begin｜>import numpy as np
from numpy import cumsum, sum, searchsorted
from numpy.random import rand
import math
import utils
import core.sentence as sentence
import core.markovchain as mc
import logging
logger = logging.getLogger(__name__)
# Dialogue making class. Need to review where to return a string, where to return a list of tokens, etc.
# setters: list of speakers, pronouns, priors etc.
# random transitions
# Internal: build list of structures:
# e.g.{:speaker_name "Alice", :speaker_pronoun "she", :speaker_str "she", :speech_verb "said", :position "end"}
# Then end with fn that maps that out to a suitable string
# e.g. "<SPEECH>, she said."
# External bit then replaces <SPEECH> with a markov-chain-generated sentence (or several).
class dialogue_maker(object):
"""Class to handle creating dialogue based on a list of speakers and a sentence generator."""
def __init__(self, names, pronouns, mc):
self.speakers = [{"name": n, "pronoun": p} for n, p in list(zip(names, pronouns))]
self._transitions = self.make_transition_probs()
self._speech_acts = ["said", "whispered", "shouted", "cried"]
self._acts_transitions = [25, 2, 2, 2]
self.mc = mc
# self.seeds = seeds
self.target_len = np.random.randint(5, 50, size=len(names)) # rough words per sentence
def make_transition_probs(self):
"""Make transition matrix between speakers, with random symmetric biases added in"""
n = len(self.speakers) # TODO why this line ???
transitions = np.random.randint(5, size=(n, n)) + 1
transitions += transitions.transpose()
for i in range(0, math.floor(n / 2)):
s1 = np.random.randint(n)
s2 = np.random.randint(n)
transitions[s1][s2] += 10
transitions[s2][s1] += 8
return(transitions)
def after(self, speaker_id):
"""Pick next person to speak"""
row = self._transitions[speaker_id]
sucessor = searchsorted(cumsum(row), rand() * sum(row))
return sucessor
def speaker_sequence(self, speaker_id, n):
"""Random walk through transitions matrix to produce a sequence of speaker ids"""
seq = []
for i in range(n):
seq.append(speaker_id)
speaker_id = self.after(speaker_id)
return seq
def speech_sequence(self, n):
speech_acts_seq = []
next_speech_id = 0
for i in range(n):
next_speech_id = searchsorted(cumsum(self._acts_transitions), rand() * sum(self._acts_transitions))
speech_acts_seq.append(self._speech_acts[next_speech_id])
return speech_acts_seq
def seq_to_names(self, sequence):
return([self.speakers[id] for id in sequence])
def make_speech_bits(self, seeds):
n = len(seeds)
speaker_id = self.speaker_sequence(0, n)
speech_acts_seq = self.speech_sequence(n)
bits = []
ss = sentence.SentenceMaker(self.mc)
for i in range(n):
sent_toks = ss.generate_sentence_tokens([seeds[i]], self.target_len[speaker_id[i]])
sent_toks = ss.polish_sentence(sent_toks)
bits.append({'speaker_name': self.speakers[speaker_id[i]]["name"],
'speech_act': speech_acts_seq[speaker_id[i]],
'seq_id': speaker_id[i],
'speech': sent_toks,
'paragraph': True})
return(bits)
def simplify(self, seq_map):
"Take a sequence of speech parts and make more natural by removing name reptition etc."
for i in range(0, len(seq_map)):
seq_map[i]['speaker_str'] = seq_map[i]['speaker_name'] # default
# Same speaker contiues:
if i > 0 and seq_map[i]['seq_id'] == seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = False
else:
if i > 1 and seq_map[i]['seq_id'] == seq_map[i - 2]['seq_id'] \
and seq_map[i]['seq_id'] != seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = True
return seq_map
def report_seq(self, seq_map):
"""Convert sequence of speeches to a tokens."""
sents = []
for i in range(0, len(seq_map)):
if seq_map[i]['paragraph']:
# text += "\n "
quote_start = '"'
else:
quote_start = ""
if i > len(seq_map) - 2 or seq_map[i + 1]['paragraph']:
quote_end = '"'
else:
quote_end = " "
if len(seq_map[i]['speech_act']) > 0:
speech_act = seq_map[i]['speech_act'] + ","
else:
<|fim_middle|>
tokens = [utils.START_TOKEN]
tokens.append(seq_map[i]['speaker_str'])
tokens.append(speech_act)
tokens.append(quote_start)
tokens.extend(seq_map[i]['speech'][1:-1])
tokens.append(quote_end)
tokens.append(utils.END_TOKEN)
sents.append(tokens)
return sents
def make_dialogue(self, seeds):
"""Returns a list of sentences, each being a list of tokens."""
acts = self.make_speech_bits(seeds)
seq_map = self.simplify(acts)
sents = self.report_seq(seq_map)
return(sents)
def dev():
import knowledge.names as names
mcW = mc.MarkovChain()
nm = names.NameMaker()
speakers = [nm.random_person() for i in range(1, 4)]
dm = dialogue_maker([n['name'] for n in speakers], [n['pronoun'] for n in speakers], mcW)
dlg = dm.make_dialogue(["dog", "run", "spot"])
print(dlg)
<｜fim▁end｜> | speech_act = seq_map[i]['speech_act'] |
<|file_name|>dialogue.py<|end_file_name|><｜fim▁begin｜>import numpy as np
from numpy import cumsum, sum, searchsorted
from numpy.random import rand
import math
import utils
import core.sentence as sentence
import core.markovchain as mc
import logging
logger = logging.getLogger(__name__)
# Dialogue making class. Need to review where to return a string, where to return a list of tokens, etc.
# setters: list of speakers, pronouns, priors etc.
# random transitions
# Internal: build list of structures:
# e.g.{:speaker_name "Alice", :speaker_pronoun "she", :speaker_str "she", :speech_verb "said", :position "end"}
# Then end with fn that maps that out to a suitable string
# e.g. "<SPEECH>, she said."
# External bit then replaces <SPEECH> with a markov-chain-generated sentence (or several).
class dialogue_maker(object):
"""Class to handle creating dialogue based on a list of speakers and a sentence generator."""
def <|fim_middle|>(self, names, pronouns, mc):
self.speakers = [{"name": n, "pronoun": p} for n, p in list(zip(names, pronouns))]
self._transitions = self.make_transition_probs()
self._speech_acts = ["said", "whispered", "shouted", "cried"]
self._acts_transitions = [25, 2, 2, 2]
self.mc = mc
# self.seeds = seeds
self.target_len = np.random.randint(5, 50, size=len(names)) # rough words per sentence
def make_transition_probs(self):
"""Make transition matrix between speakers, with random symmetric biases added in"""
n = len(self.speakers) # TODO why this line ???
transitions = np.random.randint(5, size=(n, n)) + 1
transitions += transitions.transpose()
for i in range(0, math.floor(n / 2)):
s1 = np.random.randint(n)
s2 = np.random.randint(n)
transitions[s1][s2] += 10
transitions[s2][s1] += 8
return(transitions)
def after(self, speaker_id):
"""Pick next person to speak"""
row = self._transitions[speaker_id]
sucessor = searchsorted(cumsum(row), rand() * sum(row))
return sucessor
def speaker_sequence(self, speaker_id, n):
"""Random walk through transitions matrix to produce a sequence of speaker ids"""
seq = []
for i in range(n):
seq.append(speaker_id)
speaker_id = self.after(speaker_id)
return seq
def speech_sequence(self, n):
speech_acts_seq = []
next_speech_id = 0
for i in range(n):
next_speech_id = searchsorted(cumsum(self._acts_transitions), rand() * sum(self._acts_transitions))
speech_acts_seq.append(self._speech_acts[next_speech_id])
return speech_acts_seq
def seq_to_names(self, sequence):
return([self.speakers[id] for id in sequence])
def make_speech_bits(self, seeds):
n = len(seeds)
speaker_id = self.speaker_sequence(0, n)
speech_acts_seq = self.speech_sequence(n)
bits = []
ss = sentence.SentenceMaker(self.mc)
for i in range(n):
sent_toks = ss.generate_sentence_tokens([seeds[i]], self.target_len[speaker_id[i]])
sent_toks = ss.polish_sentence(sent_toks)
bits.append({'speaker_name': self.speakers[speaker_id[i]]["name"],
'speech_act': speech_acts_seq[speaker_id[i]],
'seq_id': speaker_id[i],
'speech': sent_toks,
'paragraph': True})
return(bits)
def simplify(self, seq_map):
"Take a sequence of speech parts and make more natural by removing name reptition etc."
for i in range(0, len(seq_map)):
seq_map[i]['speaker_str'] = seq_map[i]['speaker_name'] # default
# Same speaker contiues:
if i > 0 and seq_map[i]['seq_id'] == seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = False
else:
if i > 1 and seq_map[i]['seq_id'] == seq_map[i - 2]['seq_id'] \
and seq_map[i]['seq_id'] != seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = True
return seq_map
def report_seq(self, seq_map):
"""Convert sequence of speeches to a tokens."""
sents = []
for i in range(0, len(seq_map)):
if seq_map[i]['paragraph']:
# text += "\n "
quote_start = '"'
else:
quote_start = ""
if i > len(seq_map) - 2 or seq_map[i + 1]['paragraph']:
quote_end = '"'
else:
quote_end = " "
if len(seq_map[i]['speech_act']) > 0:
speech_act = seq_map[i]['speech_act'] + ","
else:
speech_act = seq_map[i]['speech_act']
tokens = [utils.START_TOKEN]
tokens.append(seq_map[i]['speaker_str'])
tokens.append(speech_act)
tokens.append(quote_start)
tokens.extend(seq_map[i]['speech'][1:-1])
tokens.append(quote_end)
tokens.append(utils.END_TOKEN)
sents.append(tokens)
return sents
def make_dialogue(self, seeds):
"""Returns a list of sentences, each being a list of tokens."""
acts = self.make_speech_bits(seeds)
seq_map = self.simplify(acts)
sents = self.report_seq(seq_map)
return(sents)
def dev():
import knowledge.names as names
mcW = mc.MarkovChain()
nm = names.NameMaker()
speakers = [nm.random_person() for i in range(1, 4)]
dm = dialogue_maker([n['name'] for n in speakers], [n['pronoun'] for n in speakers], mcW)
dlg = dm.make_dialogue(["dog", "run", "spot"])
print(dlg)
<｜fim▁end｜> | __init__ |
<|file_name|>dialogue.py<|end_file_name|><｜fim▁begin｜>import numpy as np
from numpy import cumsum, sum, searchsorted
from numpy.random import rand
import math
import utils
import core.sentence as sentence
import core.markovchain as mc
import logging
logger = logging.getLogger(__name__)
# Dialogue making class. Need to review where to return a string, where to return a list of tokens, etc.
# setters: list of speakers, pronouns, priors etc.
# random transitions
# Internal: build list of structures:
# e.g.{:speaker_name "Alice", :speaker_pronoun "she", :speaker_str "she", :speech_verb "said", :position "end"}
# Then end with fn that maps that out to a suitable string
# e.g. "<SPEECH>, she said."
# External bit then replaces <SPEECH> with a markov-chain-generated sentence (or several).
class dialogue_maker(object):
"""Class to handle creating dialogue based on a list of speakers and a sentence generator."""
def __init__(self, names, pronouns, mc):
self.speakers = [{"name": n, "pronoun": p} for n, p in list(zip(names, pronouns))]
self._transitions = self.make_transition_probs()
self._speech_acts = ["said", "whispered", "shouted", "cried"]
self._acts_transitions = [25, 2, 2, 2]
self.mc = mc
# self.seeds = seeds
self.target_len = np.random.randint(5, 50, size=len(names)) # rough words per sentence
def <|fim_middle|>(self):
"""Make transition matrix between speakers, with random symmetric biases added in"""
n = len(self.speakers) # TODO why this line ???
transitions = np.random.randint(5, size=(n, n)) + 1
transitions += transitions.transpose()
for i in range(0, math.floor(n / 2)):
s1 = np.random.randint(n)
s2 = np.random.randint(n)
transitions[s1][s2] += 10
transitions[s2][s1] += 8
return(transitions)
def after(self, speaker_id):
"""Pick next person to speak"""
row = self._transitions[speaker_id]
sucessor = searchsorted(cumsum(row), rand() * sum(row))
return sucessor
def speaker_sequence(self, speaker_id, n):
"""Random walk through transitions matrix to produce a sequence of speaker ids"""
seq = []
for i in range(n):
seq.append(speaker_id)
speaker_id = self.after(speaker_id)
return seq
def speech_sequence(self, n):
speech_acts_seq = []
next_speech_id = 0
for i in range(n):
next_speech_id = searchsorted(cumsum(self._acts_transitions), rand() * sum(self._acts_transitions))
speech_acts_seq.append(self._speech_acts[next_speech_id])
return speech_acts_seq
def seq_to_names(self, sequence):
return([self.speakers[id] for id in sequence])
def make_speech_bits(self, seeds):
n = len(seeds)
speaker_id = self.speaker_sequence(0, n)
speech_acts_seq = self.speech_sequence(n)
bits = []
ss = sentence.SentenceMaker(self.mc)
for i in range(n):
sent_toks = ss.generate_sentence_tokens([seeds[i]], self.target_len[speaker_id[i]])
sent_toks = ss.polish_sentence(sent_toks)
bits.append({'speaker_name': self.speakers[speaker_id[i]]["name"],
'speech_act': speech_acts_seq[speaker_id[i]],
'seq_id': speaker_id[i],
'speech': sent_toks,
'paragraph': True})
return(bits)
def simplify(self, seq_map):
"Take a sequence of speech parts and make more natural by removing name reptition etc."
for i in range(0, len(seq_map)):
seq_map[i]['speaker_str'] = seq_map[i]['speaker_name'] # default
# Same speaker contiues:
if i > 0 and seq_map[i]['seq_id'] == seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = False
else:
if i > 1 and seq_map[i]['seq_id'] == seq_map[i - 2]['seq_id'] \
and seq_map[i]['seq_id'] != seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = True
return seq_map
def report_seq(self, seq_map):
"""Convert sequence of speeches to a tokens."""
sents = []
for i in range(0, len(seq_map)):
if seq_map[i]['paragraph']:
# text += "\n "
quote_start = '"'
else:
quote_start = ""
if i > len(seq_map) - 2 or seq_map[i + 1]['paragraph']:
quote_end = '"'
else:
quote_end = " "
if len(seq_map[i]['speech_act']) > 0:
speech_act = seq_map[i]['speech_act'] + ","
else:
speech_act = seq_map[i]['speech_act']
tokens = [utils.START_TOKEN]
tokens.append(seq_map[i]['speaker_str'])
tokens.append(speech_act)
tokens.append(quote_start)
tokens.extend(seq_map[i]['speech'][1:-1])
tokens.append(quote_end)
tokens.append(utils.END_TOKEN)
sents.append(tokens)
return sents
def make_dialogue(self, seeds):
"""Returns a list of sentences, each being a list of tokens."""
acts = self.make_speech_bits(seeds)
seq_map = self.simplify(acts)
sents = self.report_seq(seq_map)
return(sents)
def dev():
import knowledge.names as names
mcW = mc.MarkovChain()
nm = names.NameMaker()
speakers = [nm.random_person() for i in range(1, 4)]
dm = dialogue_maker([n['name'] for n in speakers], [n['pronoun'] for n in speakers], mcW)
dlg = dm.make_dialogue(["dog", "run", "spot"])
print(dlg)
<｜fim▁end｜> | make_transition_probs |
<|file_name|>dialogue.py<|end_file_name|><｜fim▁begin｜>import numpy as np
from numpy import cumsum, sum, searchsorted
from numpy.random import rand
import math
import utils
import core.sentence as sentence
import core.markovchain as mc
import logging
logger = logging.getLogger(__name__)
# Dialogue making class. Need to review where to return a string, where to return a list of tokens, etc.
# setters: list of speakers, pronouns, priors etc.
# random transitions
# Internal: build list of structures:
# e.g.{:speaker_name "Alice", :speaker_pronoun "she", :speaker_str "she", :speech_verb "said", :position "end"}
# Then end with fn that maps that out to a suitable string
# e.g. "<SPEECH>, she said."
# External bit then replaces <SPEECH> with a markov-chain-generated sentence (or several).
class dialogue_maker(object):
"""Class to handle creating dialogue based on a list of speakers and a sentence generator."""
def __init__(self, names, pronouns, mc):
self.speakers = [{"name": n, "pronoun": p} for n, p in list(zip(names, pronouns))]
self._transitions = self.make_transition_probs()
self._speech_acts = ["said", "whispered", "shouted", "cried"]
self._acts_transitions = [25, 2, 2, 2]
self.mc = mc
# self.seeds = seeds
self.target_len = np.random.randint(5, 50, size=len(names)) # rough words per sentence
def make_transition_probs(self):
"""Make transition matrix between speakers, with random symmetric biases added in"""
n = len(self.speakers) # TODO why this line ???
transitions = np.random.randint(5, size=(n, n)) + 1
transitions += transitions.transpose()
for i in range(0, math.floor(n / 2)):
s1 = np.random.randint(n)
s2 = np.random.randint(n)
transitions[s1][s2] += 10
transitions[s2][s1] += 8
return(transitions)
def <|fim_middle|>(self, speaker_id):
"""Pick next person to speak"""
row = self._transitions[speaker_id]
sucessor = searchsorted(cumsum(row), rand() * sum(row))
return sucessor
def speaker_sequence(self, speaker_id, n):
"""Random walk through transitions matrix to produce a sequence of speaker ids"""
seq = []
for i in range(n):
seq.append(speaker_id)
speaker_id = self.after(speaker_id)
return seq
def speech_sequence(self, n):
speech_acts_seq = []
next_speech_id = 0
for i in range(n):
next_speech_id = searchsorted(cumsum(self._acts_transitions), rand() * sum(self._acts_transitions))
speech_acts_seq.append(self._speech_acts[next_speech_id])
return speech_acts_seq
def seq_to_names(self, sequence):
return([self.speakers[id] for id in sequence])
def make_speech_bits(self, seeds):
n = len(seeds)
speaker_id = self.speaker_sequence(0, n)
speech_acts_seq = self.speech_sequence(n)
bits = []
ss = sentence.SentenceMaker(self.mc)
for i in range(n):
sent_toks = ss.generate_sentence_tokens([seeds[i]], self.target_len[speaker_id[i]])
sent_toks = ss.polish_sentence(sent_toks)
bits.append({'speaker_name': self.speakers[speaker_id[i]]["name"],
'speech_act': speech_acts_seq[speaker_id[i]],
'seq_id': speaker_id[i],
'speech': sent_toks,
'paragraph': True})
return(bits)
def simplify(self, seq_map):
"Take a sequence of speech parts and make more natural by removing name reptition etc."
for i in range(0, len(seq_map)):
seq_map[i]['speaker_str'] = seq_map[i]['speaker_name'] # default
# Same speaker contiues:
if i > 0 and seq_map[i]['seq_id'] == seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = False
else:
if i > 1 and seq_map[i]['seq_id'] == seq_map[i - 2]['seq_id'] \
and seq_map[i]['seq_id'] != seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = True
return seq_map
def report_seq(self, seq_map):
"""Convert sequence of speeches to a tokens."""
sents = []
for i in range(0, len(seq_map)):
if seq_map[i]['paragraph']:
# text += "\n "
quote_start = '"'
else:
quote_start = ""
if i > len(seq_map) - 2 or seq_map[i + 1]['paragraph']:
quote_end = '"'
else:
quote_end = " "
if len(seq_map[i]['speech_act']) > 0:
speech_act = seq_map[i]['speech_act'] + ","
else:
speech_act = seq_map[i]['speech_act']
tokens = [utils.START_TOKEN]
tokens.append(seq_map[i]['speaker_str'])
tokens.append(speech_act)
tokens.append(quote_start)
tokens.extend(seq_map[i]['speech'][1:-1])
tokens.append(quote_end)
tokens.append(utils.END_TOKEN)
sents.append(tokens)
return sents
def make_dialogue(self, seeds):
"""Returns a list of sentences, each being a list of tokens."""
acts = self.make_speech_bits(seeds)
seq_map = self.simplify(acts)
sents = self.report_seq(seq_map)
return(sents)
def dev():
import knowledge.names as names
mcW = mc.MarkovChain()
nm = names.NameMaker()
speakers = [nm.random_person() for i in range(1, 4)]
dm = dialogue_maker([n['name'] for n in speakers], [n['pronoun'] for n in speakers], mcW)
dlg = dm.make_dialogue(["dog", "run", "spot"])
print(dlg)
<｜fim▁end｜> | after |
<|file_name|>dialogue.py<|end_file_name|><｜fim▁begin｜>import numpy as np
from numpy import cumsum, sum, searchsorted
from numpy.random import rand
import math
import utils
import core.sentence as sentence
import core.markovchain as mc
import logging
logger = logging.getLogger(__name__)
# Dialogue making class. Need to review where to return a string, where to return a list of tokens, etc.
# setters: list of speakers, pronouns, priors etc.
# random transitions
# Internal: build list of structures:
# e.g.{:speaker_name "Alice", :speaker_pronoun "she", :speaker_str "she", :speech_verb "said", :position "end"}
# Then end with fn that maps that out to a suitable string
# e.g. "<SPEECH>, she said."
# External bit then replaces <SPEECH> with a markov-chain-generated sentence (or several).
class dialogue_maker(object):
"""Class to handle creating dialogue based on a list of speakers and a sentence generator."""
def __init__(self, names, pronouns, mc):
self.speakers = [{"name": n, "pronoun": p} for n, p in list(zip(names, pronouns))]
self._transitions = self.make_transition_probs()
self._speech_acts = ["said", "whispered", "shouted", "cried"]
self._acts_transitions = [25, 2, 2, 2]
self.mc = mc
# self.seeds = seeds
self.target_len = np.random.randint(5, 50, size=len(names)) # rough words per sentence
def make_transition_probs(self):
"""Make transition matrix between speakers, with random symmetric biases added in"""
n = len(self.speakers) # TODO why this line ???
transitions = np.random.randint(5, size=(n, n)) + 1
transitions += transitions.transpose()
for i in range(0, math.floor(n / 2)):
s1 = np.random.randint(n)
s2 = np.random.randint(n)
transitions[s1][s2] += 10
transitions[s2][s1] += 8
return(transitions)
def after(self, speaker_id):
"""Pick next person to speak"""
row = self._transitions[speaker_id]
sucessor = searchsorted(cumsum(row), rand() * sum(row))
return sucessor
def <|fim_middle|>(self, speaker_id, n):
"""Random walk through transitions matrix to produce a sequence of speaker ids"""
seq = []
for i in range(n):
seq.append(speaker_id)
speaker_id = self.after(speaker_id)
return seq
def speech_sequence(self, n):
speech_acts_seq = []
next_speech_id = 0
for i in range(n):
next_speech_id = searchsorted(cumsum(self._acts_transitions), rand() * sum(self._acts_transitions))
speech_acts_seq.append(self._speech_acts[next_speech_id])
return speech_acts_seq
def seq_to_names(self, sequence):
return([self.speakers[id] for id in sequence])
def make_speech_bits(self, seeds):
n = len(seeds)
speaker_id = self.speaker_sequence(0, n)
speech_acts_seq = self.speech_sequence(n)
bits = []
ss = sentence.SentenceMaker(self.mc)
for i in range(n):
sent_toks = ss.generate_sentence_tokens([seeds[i]], self.target_len[speaker_id[i]])
sent_toks = ss.polish_sentence(sent_toks)
bits.append({'speaker_name': self.speakers[speaker_id[i]]["name"],
'speech_act': speech_acts_seq[speaker_id[i]],
'seq_id': speaker_id[i],
'speech': sent_toks,
'paragraph': True})
return(bits)
def simplify(self, seq_map):
"Take a sequence of speech parts and make more natural by removing name reptition etc."
for i in range(0, len(seq_map)):
seq_map[i]['speaker_str'] = seq_map[i]['speaker_name'] # default
# Same speaker contiues:
if i > 0 and seq_map[i]['seq_id'] == seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = False
else:
if i > 1 and seq_map[i]['seq_id'] == seq_map[i - 2]['seq_id'] \
and seq_map[i]['seq_id'] != seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = True
return seq_map
def report_seq(self, seq_map):
"""Convert sequence of speeches to a tokens."""
sents = []
for i in range(0, len(seq_map)):
if seq_map[i]['paragraph']:
# text += "\n "
quote_start = '"'
else:
quote_start = ""
if i > len(seq_map) - 2 or seq_map[i + 1]['paragraph']:
quote_end = '"'
else:
quote_end = " "
if len(seq_map[i]['speech_act']) > 0:
speech_act = seq_map[i]['speech_act'] + ","
else:
speech_act = seq_map[i]['speech_act']
tokens = [utils.START_TOKEN]
tokens.append(seq_map[i]['speaker_str'])
tokens.append(speech_act)
tokens.append(quote_start)
tokens.extend(seq_map[i]['speech'][1:-1])
tokens.append(quote_end)
tokens.append(utils.END_TOKEN)
sents.append(tokens)
return sents
def make_dialogue(self, seeds):
"""Returns a list of sentences, each being a list of tokens."""
acts = self.make_speech_bits(seeds)
seq_map = self.simplify(acts)
sents = self.report_seq(seq_map)
return(sents)
def dev():
import knowledge.names as names
mcW = mc.MarkovChain()
nm = names.NameMaker()
speakers = [nm.random_person() for i in range(1, 4)]
dm = dialogue_maker([n['name'] for n in speakers], [n['pronoun'] for n in speakers], mcW)
dlg = dm.make_dialogue(["dog", "run", "spot"])
print(dlg)
<｜fim▁end｜> | speaker_sequence |
<|file_name|>dialogue.py<|end_file_name|><｜fim▁begin｜>import numpy as np
from numpy import cumsum, sum, searchsorted
from numpy.random import rand
import math
import utils
import core.sentence as sentence
import core.markovchain as mc
import logging
logger = logging.getLogger(__name__)
# Dialogue making class. Need to review where to return a string, where to return a list of tokens, etc.
# setters: list of speakers, pronouns, priors etc.
# random transitions
# Internal: build list of structures:
# e.g.{:speaker_name "Alice", :speaker_pronoun "she", :speaker_str "she", :speech_verb "said", :position "end"}
# Then end with fn that maps that out to a suitable string
# e.g. "<SPEECH>, she said."
# External bit then replaces <SPEECH> with a markov-chain-generated sentence (or several).
class dialogue_maker(object):
"""Class to handle creating dialogue based on a list of speakers and a sentence generator."""
def __init__(self, names, pronouns, mc):
self.speakers = [{"name": n, "pronoun": p} for n, p in list(zip(names, pronouns))]
self._transitions = self.make_transition_probs()
self._speech_acts = ["said", "whispered", "shouted", "cried"]
self._acts_transitions = [25, 2, 2, 2]
self.mc = mc
# self.seeds = seeds
self.target_len = np.random.randint(5, 50, size=len(names)) # rough words per sentence
def make_transition_probs(self):
"""Make transition matrix between speakers, with random symmetric biases added in"""
n = len(self.speakers) # TODO why this line ???
transitions = np.random.randint(5, size=(n, n)) + 1
transitions += transitions.transpose()
for i in range(0, math.floor(n / 2)):
s1 = np.random.randint(n)
s2 = np.random.randint(n)
transitions[s1][s2] += 10
transitions[s2][s1] += 8
return(transitions)
def after(self, speaker_id):
"""Pick next person to speak"""
row = self._transitions[speaker_id]
sucessor = searchsorted(cumsum(row), rand() * sum(row))
return sucessor
def speaker_sequence(self, speaker_id, n):
"""Random walk through transitions matrix to produce a sequence of speaker ids"""
seq = []
for i in range(n):
seq.append(speaker_id)
speaker_id = self.after(speaker_id)
return seq
def <|fim_middle|>(self, n):
speech_acts_seq = []
next_speech_id = 0
for i in range(n):
next_speech_id = searchsorted(cumsum(self._acts_transitions), rand() * sum(self._acts_transitions))
speech_acts_seq.append(self._speech_acts[next_speech_id])
return speech_acts_seq
def seq_to_names(self, sequence):
return([self.speakers[id] for id in sequence])
def make_speech_bits(self, seeds):
n = len(seeds)
speaker_id = self.speaker_sequence(0, n)
speech_acts_seq = self.speech_sequence(n)
bits = []
ss = sentence.SentenceMaker(self.mc)
for i in range(n):
sent_toks = ss.generate_sentence_tokens([seeds[i]], self.target_len[speaker_id[i]])
sent_toks = ss.polish_sentence(sent_toks)
bits.append({'speaker_name': self.speakers[speaker_id[i]]["name"],
'speech_act': speech_acts_seq[speaker_id[i]],
'seq_id': speaker_id[i],
'speech': sent_toks,
'paragraph': True})
return(bits)
def simplify(self, seq_map):
"Take a sequence of speech parts and make more natural by removing name reptition etc."
for i in range(0, len(seq_map)):
seq_map[i]['speaker_str'] = seq_map[i]['speaker_name'] # default
# Same speaker contiues:
if i > 0 and seq_map[i]['seq_id'] == seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = False
else:
if i > 1 and seq_map[i]['seq_id'] == seq_map[i - 2]['seq_id'] \
and seq_map[i]['seq_id'] != seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = True
return seq_map
def report_seq(self, seq_map):
"""Convert sequence of speeches to a tokens."""
sents = []
for i in range(0, len(seq_map)):
if seq_map[i]['paragraph']:
# text += "\n "
quote_start = '"'
else:
quote_start = ""
if i > len(seq_map) - 2 or seq_map[i + 1]['paragraph']:
quote_end = '"'
else:
quote_end = " "
if len(seq_map[i]['speech_act']) > 0:
speech_act = seq_map[i]['speech_act'] + ","
else:
speech_act = seq_map[i]['speech_act']
tokens = [utils.START_TOKEN]
tokens.append(seq_map[i]['speaker_str'])
tokens.append(speech_act)
tokens.append(quote_start)
tokens.extend(seq_map[i]['speech'][1:-1])
tokens.append(quote_end)
tokens.append(utils.END_TOKEN)
sents.append(tokens)
return sents
def make_dialogue(self, seeds):
"""Returns a list of sentences, each being a list of tokens."""
acts = self.make_speech_bits(seeds)
seq_map = self.simplify(acts)
sents = self.report_seq(seq_map)
return(sents)
def dev():
import knowledge.names as names
mcW = mc.MarkovChain()
nm = names.NameMaker()
speakers = [nm.random_person() for i in range(1, 4)]
dm = dialogue_maker([n['name'] for n in speakers], [n['pronoun'] for n in speakers], mcW)
dlg = dm.make_dialogue(["dog", "run", "spot"])
print(dlg)
<｜fim▁end｜> | speech_sequence |
<|file_name|>dialogue.py<|end_file_name|><｜fim▁begin｜>import numpy as np
from numpy import cumsum, sum, searchsorted
from numpy.random import rand
import math
import utils
import core.sentence as sentence
import core.markovchain as mc
import logging
logger = logging.getLogger(__name__)
# Dialogue making class. Need to review where to return a string, where to return a list of tokens, etc.
# setters: list of speakers, pronouns, priors etc.
# random transitions
# Internal: build list of structures:
# e.g.{:speaker_name "Alice", :speaker_pronoun "she", :speaker_str "she", :speech_verb "said", :position "end"}
# Then end with fn that maps that out to a suitable string
# e.g. "<SPEECH>, she said."
# External bit then replaces <SPEECH> with a markov-chain-generated sentence (or several).
class dialogue_maker(object):
"""Class to handle creating dialogue based on a list of speakers and a sentence generator."""
def __init__(self, names, pronouns, mc):
self.speakers = [{"name": n, "pronoun": p} for n, p in list(zip(names, pronouns))]
self._transitions = self.make_transition_probs()
self._speech_acts = ["said", "whispered", "shouted", "cried"]
self._acts_transitions = [25, 2, 2, 2]
self.mc = mc
# self.seeds = seeds
self.target_len = np.random.randint(5, 50, size=len(names)) # rough words per sentence
def make_transition_probs(self):
"""Make transition matrix between speakers, with random symmetric biases added in"""
n = len(self.speakers) # TODO why this line ???
transitions = np.random.randint(5, size=(n, n)) + 1
transitions += transitions.transpose()
for i in range(0, math.floor(n / 2)):
s1 = np.random.randint(n)
s2 = np.random.randint(n)
transitions[s1][s2] += 10
transitions[s2][s1] += 8
return(transitions)
def after(self, speaker_id):
"""Pick next person to speak"""
row = self._transitions[speaker_id]
sucessor = searchsorted(cumsum(row), rand() * sum(row))
return sucessor
def speaker_sequence(self, speaker_id, n):
"""Random walk through transitions matrix to produce a sequence of speaker ids"""
seq = []
for i in range(n):
seq.append(speaker_id)
speaker_id = self.after(speaker_id)
return seq
def speech_sequence(self, n):
speech_acts_seq = []
next_speech_id = 0
for i in range(n):
next_speech_id = searchsorted(cumsum(self._acts_transitions), rand() * sum(self._acts_transitions))
speech_acts_seq.append(self._speech_acts[next_speech_id])
return speech_acts_seq
def <|fim_middle|>(self, sequence):
return([self.speakers[id] for id in sequence])
def make_speech_bits(self, seeds):
n = len(seeds)
speaker_id = self.speaker_sequence(0, n)
speech_acts_seq = self.speech_sequence(n)
bits = []
ss = sentence.SentenceMaker(self.mc)
for i in range(n):
sent_toks = ss.generate_sentence_tokens([seeds[i]], self.target_len[speaker_id[i]])
sent_toks = ss.polish_sentence(sent_toks)
bits.append({'speaker_name': self.speakers[speaker_id[i]]["name"],
'speech_act': speech_acts_seq[speaker_id[i]],
'seq_id': speaker_id[i],
'speech': sent_toks,
'paragraph': True})
return(bits)
def simplify(self, seq_map):
"Take a sequence of speech parts and make more natural by removing name reptition etc."
for i in range(0, len(seq_map)):
seq_map[i]['speaker_str'] = seq_map[i]['speaker_name'] # default
# Same speaker contiues:
if i > 0 and seq_map[i]['seq_id'] == seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = False
else:
if i > 1 and seq_map[i]['seq_id'] == seq_map[i - 2]['seq_id'] \
and seq_map[i]['seq_id'] != seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = True
return seq_map
def report_seq(self, seq_map):
"""Convert sequence of speeches to a tokens."""
sents = []
for i in range(0, len(seq_map)):
if seq_map[i]['paragraph']:
# text += "\n "
quote_start = '"'
else:
quote_start = ""
if i > len(seq_map) - 2 or seq_map[i + 1]['paragraph']:
quote_end = '"'
else:
quote_end = " "
if len(seq_map[i]['speech_act']) > 0:
speech_act = seq_map[i]['speech_act'] + ","
else:
speech_act = seq_map[i]['speech_act']
tokens = [utils.START_TOKEN]
tokens.append(seq_map[i]['speaker_str'])
tokens.append(speech_act)
tokens.append(quote_start)
tokens.extend(seq_map[i]['speech'][1:-1])
tokens.append(quote_end)
tokens.append(utils.END_TOKEN)
sents.append(tokens)
return sents
def make_dialogue(self, seeds):
"""Returns a list of sentences, each being a list of tokens."""
acts = self.make_speech_bits(seeds)
seq_map = self.simplify(acts)
sents = self.report_seq(seq_map)
return(sents)
def dev():
import knowledge.names as names
mcW = mc.MarkovChain()
nm = names.NameMaker()
speakers = [nm.random_person() for i in range(1, 4)]
dm = dialogue_maker([n['name'] for n in speakers], [n['pronoun'] for n in speakers], mcW)
dlg = dm.make_dialogue(["dog", "run", "spot"])
print(dlg)
<｜fim▁end｜> | seq_to_names |
<|file_name|>dialogue.py<|end_file_name|><｜fim▁begin｜>import numpy as np
from numpy import cumsum, sum, searchsorted
from numpy.random import rand
import math
import utils
import core.sentence as sentence
import core.markovchain as mc
import logging
logger = logging.getLogger(__name__)
# Dialogue making class. Need to review where to return a string, where to return a list of tokens, etc.
# setters: list of speakers, pronouns, priors etc.
# random transitions
# Internal: build list of structures:
# e.g.{:speaker_name "Alice", :speaker_pronoun "she", :speaker_str "she", :speech_verb "said", :position "end"}
# Then end with fn that maps that out to a suitable string
# e.g. "<SPEECH>, she said."
# External bit then replaces <SPEECH> with a markov-chain-generated sentence (or several).
class dialogue_maker(object):
"""Class to handle creating dialogue based on a list of speakers and a sentence generator."""
def __init__(self, names, pronouns, mc):
self.speakers = [{"name": n, "pronoun": p} for n, p in list(zip(names, pronouns))]
self._transitions = self.make_transition_probs()
self._speech_acts = ["said", "whispered", "shouted", "cried"]
self._acts_transitions = [25, 2, 2, 2]
self.mc = mc
# self.seeds = seeds
self.target_len = np.random.randint(5, 50, size=len(names)) # rough words per sentence
def make_transition_probs(self):
"""Make transition matrix between speakers, with random symmetric biases added in"""
n = len(self.speakers) # TODO why this line ???
transitions = np.random.randint(5, size=(n, n)) + 1
transitions += transitions.transpose()
for i in range(0, math.floor(n / 2)):
s1 = np.random.randint(n)
s2 = np.random.randint(n)
transitions[s1][s2] += 10
transitions[s2][s1] += 8
return(transitions)
def after(self, speaker_id):
"""Pick next person to speak"""
row = self._transitions[speaker_id]
sucessor = searchsorted(cumsum(row), rand() * sum(row))
return sucessor
def speaker_sequence(self, speaker_id, n):
"""Random walk through transitions matrix to produce a sequence of speaker ids"""
seq = []
for i in range(n):
seq.append(speaker_id)
speaker_id = self.after(speaker_id)
return seq
def speech_sequence(self, n):
speech_acts_seq = []
next_speech_id = 0
for i in range(n):
next_speech_id = searchsorted(cumsum(self._acts_transitions), rand() * sum(self._acts_transitions))
speech_acts_seq.append(self._speech_acts[next_speech_id])
return speech_acts_seq
def seq_to_names(self, sequence):
return([self.speakers[id] for id in sequence])
def <|fim_middle|>(self, seeds):
n = len(seeds)
speaker_id = self.speaker_sequence(0, n)
speech_acts_seq = self.speech_sequence(n)
bits = []
ss = sentence.SentenceMaker(self.mc)
for i in range(n):
sent_toks = ss.generate_sentence_tokens([seeds[i]], self.target_len[speaker_id[i]])
sent_toks = ss.polish_sentence(sent_toks)
bits.append({'speaker_name': self.speakers[speaker_id[i]]["name"],
'speech_act': speech_acts_seq[speaker_id[i]],
'seq_id': speaker_id[i],
'speech': sent_toks,
'paragraph': True})
return(bits)
def simplify(self, seq_map):
"Take a sequence of speech parts and make more natural by removing name reptition etc."
for i in range(0, len(seq_map)):
seq_map[i]['speaker_str'] = seq_map[i]['speaker_name'] # default
# Same speaker contiues:
if i > 0 and seq_map[i]['seq_id'] == seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = False
else:
if i > 1 and seq_map[i]['seq_id'] == seq_map[i - 2]['seq_id'] \
and seq_map[i]['seq_id'] != seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = True
return seq_map
def report_seq(self, seq_map):
"""Convert sequence of speeches to a tokens."""
sents = []
for i in range(0, len(seq_map)):
if seq_map[i]['paragraph']:
# text += "\n "
quote_start = '"'
else:
quote_start = ""
if i > len(seq_map) - 2 or seq_map[i + 1]['paragraph']:
quote_end = '"'
else:
quote_end = " "
if len(seq_map[i]['speech_act']) > 0:
speech_act = seq_map[i]['speech_act'] + ","
else:
speech_act = seq_map[i]['speech_act']
tokens = [utils.START_TOKEN]
tokens.append(seq_map[i]['speaker_str'])
tokens.append(speech_act)
tokens.append(quote_start)
tokens.extend(seq_map[i]['speech'][1:-1])
tokens.append(quote_end)
tokens.append(utils.END_TOKEN)
sents.append(tokens)
return sents
def make_dialogue(self, seeds):
"""Returns a list of sentences, each being a list of tokens."""
acts = self.make_speech_bits(seeds)
seq_map = self.simplify(acts)
sents = self.report_seq(seq_map)
return(sents)
def dev():
import knowledge.names as names
mcW = mc.MarkovChain()
nm = names.NameMaker()
speakers = [nm.random_person() for i in range(1, 4)]
dm = dialogue_maker([n['name'] for n in speakers], [n['pronoun'] for n in speakers], mcW)
dlg = dm.make_dialogue(["dog", "run", "spot"])
print(dlg)
<｜fim▁end｜> | make_speech_bits |
<|file_name|>dialogue.py<|end_file_name|><｜fim▁begin｜>import numpy as np
from numpy import cumsum, sum, searchsorted
from numpy.random import rand
import math
import utils
import core.sentence as sentence
import core.markovchain as mc
import logging
logger = logging.getLogger(__name__)
# Dialogue making class. Need to review where to return a string, where to return a list of tokens, etc.
# setters: list of speakers, pronouns, priors etc.
# random transitions
# Internal: build list of structures:
# e.g.{:speaker_name "Alice", :speaker_pronoun "she", :speaker_str "she", :speech_verb "said", :position "end"}
# Then end with fn that maps that out to a suitable string
# e.g. "<SPEECH>, she said."
# External bit then replaces <SPEECH> with a markov-chain-generated sentence (or several).
class dialogue_maker(object):
"""Class to handle creating dialogue based on a list of speakers and a sentence generator."""
def __init__(self, names, pronouns, mc):
self.speakers = [{"name": n, "pronoun": p} for n, p in list(zip(names, pronouns))]
self._transitions = self.make_transition_probs()
self._speech_acts = ["said", "whispered", "shouted", "cried"]
self._acts_transitions = [25, 2, 2, 2]
self.mc = mc
# self.seeds = seeds
self.target_len = np.random.randint(5, 50, size=len(names)) # rough words per sentence
def make_transition_probs(self):
"""Make transition matrix between speakers, with random symmetric biases added in"""
n = len(self.speakers) # TODO why this line ???
transitions = np.random.randint(5, size=(n, n)) + 1
transitions += transitions.transpose()
for i in range(0, math.floor(n / 2)):
s1 = np.random.randint(n)
s2 = np.random.randint(n)
transitions[s1][s2] += 10
transitions[s2][s1] += 8
return(transitions)
def after(self, speaker_id):
"""Pick next person to speak"""
row = self._transitions[speaker_id]
sucessor = searchsorted(cumsum(row), rand() * sum(row))
return sucessor
def speaker_sequence(self, speaker_id, n):
"""Random walk through transitions matrix to produce a sequence of speaker ids"""
seq = []
for i in range(n):
seq.append(speaker_id)
speaker_id = self.after(speaker_id)
return seq
def speech_sequence(self, n):
speech_acts_seq = []
next_speech_id = 0
for i in range(n):
next_speech_id = searchsorted(cumsum(self._acts_transitions), rand() * sum(self._acts_transitions))
speech_acts_seq.append(self._speech_acts[next_speech_id])
return speech_acts_seq
def seq_to_names(self, sequence):
return([self.speakers[id] for id in sequence])
def make_speech_bits(self, seeds):
n = len(seeds)
speaker_id = self.speaker_sequence(0, n)
speech_acts_seq = self.speech_sequence(n)
bits = []
ss = sentence.SentenceMaker(self.mc)
for i in range(n):
sent_toks = ss.generate_sentence_tokens([seeds[i]], self.target_len[speaker_id[i]])
sent_toks = ss.polish_sentence(sent_toks)
bits.append({'speaker_name': self.speakers[speaker_id[i]]["name"],
'speech_act': speech_acts_seq[speaker_id[i]],
'seq_id': speaker_id[i],
'speech': sent_toks,
'paragraph': True})
return(bits)
def <|fim_middle|>(self, seq_map):
"Take a sequence of speech parts and make more natural by removing name reptition etc."
for i in range(0, len(seq_map)):
seq_map[i]['speaker_str'] = seq_map[i]['speaker_name'] # default
# Same speaker contiues:
if i > 0 and seq_map[i]['seq_id'] == seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = False
else:
if i > 1 and seq_map[i]['seq_id'] == seq_map[i - 2]['seq_id'] \
and seq_map[i]['seq_id'] != seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = True
return seq_map
def report_seq(self, seq_map):
"""Convert sequence of speeches to a tokens."""
sents = []
for i in range(0, len(seq_map)):
if seq_map[i]['paragraph']:
# text += "\n "
quote_start = '"'
else:
quote_start = ""
if i > len(seq_map) - 2 or seq_map[i + 1]['paragraph']:
quote_end = '"'
else:
quote_end = " "
if len(seq_map[i]['speech_act']) > 0:
speech_act = seq_map[i]['speech_act'] + ","
else:
speech_act = seq_map[i]['speech_act']
tokens = [utils.START_TOKEN]
tokens.append(seq_map[i]['speaker_str'])
tokens.append(speech_act)
tokens.append(quote_start)
tokens.extend(seq_map[i]['speech'][1:-1])
tokens.append(quote_end)
tokens.append(utils.END_TOKEN)
sents.append(tokens)
return sents
def make_dialogue(self, seeds):
"""Returns a list of sentences, each being a list of tokens."""
acts = self.make_speech_bits(seeds)
seq_map = self.simplify(acts)
sents = self.report_seq(seq_map)
return(sents)
def dev():
import knowledge.names as names
mcW = mc.MarkovChain()
nm = names.NameMaker()
speakers = [nm.random_person() for i in range(1, 4)]
dm = dialogue_maker([n['name'] for n in speakers], [n['pronoun'] for n in speakers], mcW)
dlg = dm.make_dialogue(["dog", "run", "spot"])
print(dlg)
<｜fim▁end｜> | simplify |
<|file_name|>dialogue.py<|end_file_name|><｜fim▁begin｜>import numpy as np
from numpy import cumsum, sum, searchsorted
from numpy.random import rand
import math
import utils
import core.sentence as sentence
import core.markovchain as mc
import logging
logger = logging.getLogger(__name__)
# Dialogue making class. Need to review where to return a string, where to return a list of tokens, etc.
# setters: list of speakers, pronouns, priors etc.
# random transitions
# Internal: build list of structures:
# e.g.{:speaker_name "Alice", :speaker_pronoun "she", :speaker_str "she", :speech_verb "said", :position "end"}
# Then end with fn that maps that out to a suitable string
# e.g. "<SPEECH>, she said."
# External bit then replaces <SPEECH> with a markov-chain-generated sentence (or several).
class dialogue_maker(object):
"""Class to handle creating dialogue based on a list of speakers and a sentence generator."""
def __init__(self, names, pronouns, mc):
self.speakers = [{"name": n, "pronoun": p} for n, p in list(zip(names, pronouns))]
self._transitions = self.make_transition_probs()
self._speech_acts = ["said", "whispered", "shouted", "cried"]
self._acts_transitions = [25, 2, 2, 2]
self.mc = mc
# self.seeds = seeds
self.target_len = np.random.randint(5, 50, size=len(names)) # rough words per sentence
def make_transition_probs(self):
"""Make transition matrix between speakers, with random symmetric biases added in"""
n = len(self.speakers) # TODO why this line ???
transitions = np.random.randint(5, size=(n, n)) + 1
transitions += transitions.transpose()
for i in range(0, math.floor(n / 2)):
s1 = np.random.randint(n)
s2 = np.random.randint(n)
transitions[s1][s2] += 10
transitions[s2][s1] += 8
return(transitions)
def after(self, speaker_id):
"""Pick next person to speak"""
row = self._transitions[speaker_id]
sucessor = searchsorted(cumsum(row), rand() * sum(row))
return sucessor
def speaker_sequence(self, speaker_id, n):
"""Random walk through transitions matrix to produce a sequence of speaker ids"""
seq = []
for i in range(n):
seq.append(speaker_id)
speaker_id = self.after(speaker_id)
return seq
def speech_sequence(self, n):
speech_acts_seq = []
next_speech_id = 0
for i in range(n):
next_speech_id = searchsorted(cumsum(self._acts_transitions), rand() * sum(self._acts_transitions))
speech_acts_seq.append(self._speech_acts[next_speech_id])
return speech_acts_seq
def seq_to_names(self, sequence):
return([self.speakers[id] for id in sequence])
def make_speech_bits(self, seeds):
n = len(seeds)
speaker_id = self.speaker_sequence(0, n)
speech_acts_seq = self.speech_sequence(n)
bits = []
ss = sentence.SentenceMaker(self.mc)
for i in range(n):
sent_toks = ss.generate_sentence_tokens([seeds[i]], self.target_len[speaker_id[i]])
sent_toks = ss.polish_sentence(sent_toks)
bits.append({'speaker_name': self.speakers[speaker_id[i]]["name"],
'speech_act': speech_acts_seq[speaker_id[i]],
'seq_id': speaker_id[i],
'speech': sent_toks,
'paragraph': True})
return(bits)
def simplify(self, seq_map):
"Take a sequence of speech parts and make more natural by removing name reptition etc."
for i in range(0, len(seq_map)):
seq_map[i]['speaker_str'] = seq_map[i]['speaker_name'] # default
# Same speaker contiues:
if i > 0 and seq_map[i]['seq_id'] == seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = False
else:
if i > 1 and seq_map[i]['seq_id'] == seq_map[i - 2]['seq_id'] \
and seq_map[i]['seq_id'] != seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = True
return seq_map
def <|fim_middle|>(self, seq_map):
"""Convert sequence of speeches to a tokens."""
sents = []
for i in range(0, len(seq_map)):
if seq_map[i]['paragraph']:
# text += "\n "
quote_start = '"'
else:
quote_start = ""
if i > len(seq_map) - 2 or seq_map[i + 1]['paragraph']:
quote_end = '"'
else:
quote_end = " "
if len(seq_map[i]['speech_act']) > 0:
speech_act = seq_map[i]['speech_act'] + ","
else:
speech_act = seq_map[i]['speech_act']
tokens = [utils.START_TOKEN]
tokens.append(seq_map[i]['speaker_str'])
tokens.append(speech_act)
tokens.append(quote_start)
tokens.extend(seq_map[i]['speech'][1:-1])
tokens.append(quote_end)
tokens.append(utils.END_TOKEN)
sents.append(tokens)
return sents
def make_dialogue(self, seeds):
"""Returns a list of sentences, each being a list of tokens."""
acts = self.make_speech_bits(seeds)
seq_map = self.simplify(acts)
sents = self.report_seq(seq_map)
return(sents)
def dev():
import knowledge.names as names
mcW = mc.MarkovChain()
nm = names.NameMaker()
speakers = [nm.random_person() for i in range(1, 4)]
dm = dialogue_maker([n['name'] for n in speakers], [n['pronoun'] for n in speakers], mcW)
dlg = dm.make_dialogue(["dog", "run", "spot"])
print(dlg)
<｜fim▁end｜> | report_seq |
<|file_name|>dialogue.py<|end_file_name|><｜fim▁begin｜>import numpy as np
from numpy import cumsum, sum, searchsorted
from numpy.random import rand
import math
import utils
import core.sentence as sentence
import core.markovchain as mc
import logging
logger = logging.getLogger(__name__)
# Dialogue making class. Need to review where to return a string, where to return a list of tokens, etc.
# setters: list of speakers, pronouns, priors etc.
# random transitions
# Internal: build list of structures:
# e.g.{:speaker_name "Alice", :speaker_pronoun "she", :speaker_str "she", :speech_verb "said", :position "end"}
# Then end with fn that maps that out to a suitable string
# e.g. "<SPEECH>, she said."
# External bit then replaces <SPEECH> with a markov-chain-generated sentence (or several).
class dialogue_maker(object):
"""Class to handle creating dialogue based on a list of speakers and a sentence generator."""
def __init__(self, names, pronouns, mc):
self.speakers = [{"name": n, "pronoun": p} for n, p in list(zip(names, pronouns))]
self._transitions = self.make_transition_probs()
self._speech_acts = ["said", "whispered", "shouted", "cried"]
self._acts_transitions = [25, 2, 2, 2]
self.mc = mc
# self.seeds = seeds
self.target_len = np.random.randint(5, 50, size=len(names)) # rough words per sentence
def make_transition_probs(self):
"""Make transition matrix between speakers, with random symmetric biases added in"""
n = len(self.speakers) # TODO why this line ???
transitions = np.random.randint(5, size=(n, n)) + 1
transitions += transitions.transpose()
for i in range(0, math.floor(n / 2)):
s1 = np.random.randint(n)
s2 = np.random.randint(n)
transitions[s1][s2] += 10
transitions[s2][s1] += 8
return(transitions)
def after(self, speaker_id):
"""Pick next person to speak"""
row = self._transitions[speaker_id]
sucessor = searchsorted(cumsum(row), rand() * sum(row))
return sucessor
def speaker_sequence(self, speaker_id, n):
"""Random walk through transitions matrix to produce a sequence of speaker ids"""
seq = []
for i in range(n):
seq.append(speaker_id)
speaker_id = self.after(speaker_id)
return seq
def speech_sequence(self, n):
speech_acts_seq = []
next_speech_id = 0
for i in range(n):
next_speech_id = searchsorted(cumsum(self._acts_transitions), rand() * sum(self._acts_transitions))
speech_acts_seq.append(self._speech_acts[next_speech_id])
return speech_acts_seq
def seq_to_names(self, sequence):
return([self.speakers[id] for id in sequence])
def make_speech_bits(self, seeds):
n = len(seeds)
speaker_id = self.speaker_sequence(0, n)
speech_acts_seq = self.speech_sequence(n)
bits = []
ss = sentence.SentenceMaker(self.mc)
for i in range(n):
sent_toks = ss.generate_sentence_tokens([seeds[i]], self.target_len[speaker_id[i]])
sent_toks = ss.polish_sentence(sent_toks)
bits.append({'speaker_name': self.speakers[speaker_id[i]]["name"],
'speech_act': speech_acts_seq[speaker_id[i]],
'seq_id': speaker_id[i],
'speech': sent_toks,
'paragraph': True})
return(bits)
def simplify(self, seq_map):
"Take a sequence of speech parts and make more natural by removing name reptition etc."
for i in range(0, len(seq_map)):
seq_map[i]['speaker_str'] = seq_map[i]['speaker_name'] # default
# Same speaker contiues:
if i > 0 and seq_map[i]['seq_id'] == seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = False
else:
if i > 1 and seq_map[i]['seq_id'] == seq_map[i - 2]['seq_id'] \
and seq_map[i]['seq_id'] != seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = True
return seq_map
def report_seq(self, seq_map):
"""Convert sequence of speeches to a tokens."""
sents = []
for i in range(0, len(seq_map)):
if seq_map[i]['paragraph']:
# text += "\n "
quote_start = '"'
else:
quote_start = ""
if i > len(seq_map) - 2 or seq_map[i + 1]['paragraph']:
quote_end = '"'
else:
quote_end = " "
if len(seq_map[i]['speech_act']) > 0:
speech_act = seq_map[i]['speech_act'] + ","
else:
speech_act = seq_map[i]['speech_act']
tokens = [utils.START_TOKEN]
tokens.append(seq_map[i]['speaker_str'])
tokens.append(speech_act)
tokens.append(quote_start)
tokens.extend(seq_map[i]['speech'][1:-1])
tokens.append(quote_end)
tokens.append(utils.END_TOKEN)
sents.append(tokens)
return sents
def <|fim_middle|>(self, seeds):
"""Returns a list of sentences, each being a list of tokens."""
acts = self.make_speech_bits(seeds)
seq_map = self.simplify(acts)
sents = self.report_seq(seq_map)
return(sents)
def dev():
import knowledge.names as names
mcW = mc.MarkovChain()
nm = names.NameMaker()
speakers = [nm.random_person() for i in range(1, 4)]
dm = dialogue_maker([n['name'] for n in speakers], [n['pronoun'] for n in speakers], mcW)
dlg = dm.make_dialogue(["dog", "run", "spot"])
print(dlg)
<｜fim▁end｜> | make_dialogue |
<|file_name|>dialogue.py<|end_file_name|><｜fim▁begin｜>import numpy as np
from numpy import cumsum, sum, searchsorted
from numpy.random import rand
import math
import utils
import core.sentence as sentence
import core.markovchain as mc
import logging
logger = logging.getLogger(__name__)
# Dialogue making class. Need to review where to return a string, where to return a list of tokens, etc.
# setters: list of speakers, pronouns, priors etc.
# random transitions
# Internal: build list of structures:
# e.g.{:speaker_name "Alice", :speaker_pronoun "she", :speaker_str "she", :speech_verb "said", :position "end"}
# Then end with fn that maps that out to a suitable string
# e.g. "<SPEECH>, she said."
# External bit then replaces <SPEECH> with a markov-chain-generated sentence (or several).
class dialogue_maker(object):
"""Class to handle creating dialogue based on a list of speakers and a sentence generator."""
def __init__(self, names, pronouns, mc):
self.speakers = [{"name": n, "pronoun": p} for n, p in list(zip(names, pronouns))]
self._transitions = self.make_transition_probs()
self._speech_acts = ["said", "whispered", "shouted", "cried"]
self._acts_transitions = [25, 2, 2, 2]
self.mc = mc
# self.seeds = seeds
self.target_len = np.random.randint(5, 50, size=len(names)) # rough words per sentence
def make_transition_probs(self):
"""Make transition matrix between speakers, with random symmetric biases added in"""
n = len(self.speakers) # TODO why this line ???
transitions = np.random.randint(5, size=(n, n)) + 1
transitions += transitions.transpose()
for i in range(0, math.floor(n / 2)):
s1 = np.random.randint(n)
s2 = np.random.randint(n)
transitions[s1][s2] += 10
transitions[s2][s1] += 8
return(transitions)
def after(self, speaker_id):
"""Pick next person to speak"""
row = self._transitions[speaker_id]
sucessor = searchsorted(cumsum(row), rand() * sum(row))
return sucessor
def speaker_sequence(self, speaker_id, n):
"""Random walk through transitions matrix to produce a sequence of speaker ids"""
seq = []
for i in range(n):
seq.append(speaker_id)
speaker_id = self.after(speaker_id)
return seq
def speech_sequence(self, n):
speech_acts_seq = []
next_speech_id = 0
for i in range(n):
next_speech_id = searchsorted(cumsum(self._acts_transitions), rand() * sum(self._acts_transitions))
speech_acts_seq.append(self._speech_acts[next_speech_id])
return speech_acts_seq
def seq_to_names(self, sequence):
return([self.speakers[id] for id in sequence])
def make_speech_bits(self, seeds):
n = len(seeds)
speaker_id = self.speaker_sequence(0, n)
speech_acts_seq = self.speech_sequence(n)
bits = []
ss = sentence.SentenceMaker(self.mc)
for i in range(n):
sent_toks = ss.generate_sentence_tokens([seeds[i]], self.target_len[speaker_id[i]])
sent_toks = ss.polish_sentence(sent_toks)
bits.append({'speaker_name': self.speakers[speaker_id[i]]["name"],
'speech_act': speech_acts_seq[speaker_id[i]],
'seq_id': speaker_id[i],
'speech': sent_toks,
'paragraph': True})
return(bits)
def simplify(self, seq_map):
"Take a sequence of speech parts and make more natural by removing name reptition etc."
for i in range(0, len(seq_map)):
seq_map[i]['speaker_str'] = seq_map[i]['speaker_name'] # default
# Same speaker contiues:
if i > 0 and seq_map[i]['seq_id'] == seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = False
else:
if i > 1 and seq_map[i]['seq_id'] == seq_map[i - 2]['seq_id'] \
and seq_map[i]['seq_id'] != seq_map[i - 1]['seq_id']:
seq_map[i]['speaker_str'] = ""
seq_map[i]['speech_act'] = ""
seq_map[i]['paragraph'] = True
return seq_map
def report_seq(self, seq_map):
"""Convert sequence of speeches to a tokens."""
sents = []
for i in range(0, len(seq_map)):
if seq_map[i]['paragraph']:
# text += "\n "
quote_start = '"'
else:
quote_start = ""
if i > len(seq_map) - 2 or seq_map[i + 1]['paragraph']:
quote_end = '"'
else:
quote_end = " "
if len(seq_map[i]['speech_act']) > 0:
speech_act = seq_map[i]['speech_act'] + ","
else:
speech_act = seq_map[i]['speech_act']
tokens = [utils.START_TOKEN]
tokens.append(seq_map[i]['speaker_str'])
tokens.append(speech_act)
tokens.append(quote_start)
tokens.extend(seq_map[i]['speech'][1:-1])
tokens.append(quote_end)
tokens.append(utils.END_TOKEN)
sents.append(tokens)
return sents
def make_dialogue(self, seeds):
"""Returns a list of sentences, each being a list of tokens."""
acts = self.make_speech_bits(seeds)
seq_map = self.simplify(acts)
sents = self.report_seq(seq_map)
return(sents)
def <|fim_middle|>():
import knowledge.names as names
mcW = mc.MarkovChain()
nm = names.NameMaker()
speakers = [nm.random_person() for i in range(1, 4)]
dm = dialogue_maker([n['name'] for n in speakers], [n['pronoun'] for n in speakers], mcW)
dlg = dm.make_dialogue(["dog", "run", "spot"])
print(dlg)
<｜fim▁end｜> | dev |
<|file_name|>rst2xml.py<|end_file_name|><｜fim▁begin｜>#!/Users/Varun/Documents/GitHub/LockScreen/venv/bin/python
# $Id: rst2xml.py 4564 2006-05-21 20:44:42Z wiemann $
# Author: David Goodger <[email protected]>
# Copyright: This module has been placed in the public domain.
"""
A minimal front end to the Docutils Publisher, producing Docutils XML.
"""
try:
import locale
locale.setlocale(locale.LC_ALL, '')
except:
pass
from docutils.core import publish_cmdline, default_description
description = ('Generates Docutils-native XML from standalone '<｜fim▁hole｜><｜fim▁end｜> | 'reStructuredText sources. ' + default_description)
publish_cmdline(writer_name='xml', description=description) |
<|file_name|>test_fs.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from __future__ import unicode_literals
import os, sys
import tempfile
from winsys._compat import unittest
import uuid
import win32file
from winsys.tests.test_fs import utils
from winsys import fs
class TestFS (unittest.TestCase):
filenames = ["%d" % i for i in range (5)]
def setUp (self):
utils.mktemp ()
for filename in self.filenames:
with open (os.path.join (utils.TEST_ROOT, filename), "w"):
pass
def tearDown (self):
utils.rmtemp ()
def test_glob (self):<｜fim▁hole｜> def test_listdir (self):
import os
fs_version = list (fs.listdir (utils.TEST_ROOT))
os_version = os.listdir (utils.TEST_ROOT)
self.assertEquals (fs_version, os_version, "%s differs from %s" % (fs_version, os_version))
#
# All the other module-level functions are hand-offs
# to the corresponding Entry methods.
#
if __name__ == "__main__":
unittest.main ()
if sys.stdout.isatty (): raw_input ("Press enter...")<｜fim▁end｜> | import glob
pattern = os.path.join (utils.TEST_ROOT, "*")
self.assertEquals (list (fs.glob (pattern)), glob.glob (pattern))
|
<|file_name|>test_fs.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from __future__ import unicode_literals
import os, sys
import tempfile
from winsys._compat import unittest
import uuid
import win32file
from winsys.tests.test_fs import utils
from winsys import fs
class TestFS (unittest.TestCase):
<|fim_middle|>
#
# All the other module-level functions are hand-offs
# to the corresponding Entry methods.
#
if __name__ == "__main__":
unittest.main ()
if sys.stdout.isatty (): raw_input ("Press enter...")
<｜fim▁end｜> | filenames = ["%d" % i for i in range (5)]
def setUp (self):
utils.mktemp ()
for filename in self.filenames:
with open (os.path.join (utils.TEST_ROOT, filename), "w"):
pass
def tearDown (self):
utils.rmtemp ()
def test_glob (self):
import glob
pattern = os.path.join (utils.TEST_ROOT, "*")
self.assertEquals (list (fs.glob (pattern)), glob.glob (pattern))
def test_listdir (self):
import os
fs_version = list (fs.listdir (utils.TEST_ROOT))
os_version = os.listdir (utils.TEST_ROOT)
self.assertEquals (fs_version, os_version, "%s differs from %s" % (fs_version, os_version)) |
<|file_name|>test_fs.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from __future__ import unicode_literals
import os, sys
import tempfile
from winsys._compat import unittest
import uuid
import win32file
from winsys.tests.test_fs import utils
from winsys import fs
class TestFS (unittest.TestCase):
filenames = ["%d" % i for i in range (5)]
def setUp (self):
<|fim_middle|>
def tearDown (self):
utils.rmtemp ()
def test_glob (self):
import glob
pattern = os.path.join (utils.TEST_ROOT, "*")
self.assertEquals (list (fs.glob (pattern)), glob.glob (pattern))
def test_listdir (self):
import os
fs_version = list (fs.listdir (utils.TEST_ROOT))
os_version = os.listdir (utils.TEST_ROOT)
self.assertEquals (fs_version, os_version, "%s differs from %s" % (fs_version, os_version))
#
# All the other module-level functions are hand-offs
# to the corresponding Entry methods.
#
if __name__ == "__main__":
unittest.main ()
if sys.stdout.isatty (): raw_input ("Press enter...")
<｜fim▁end｜> | utils.mktemp ()
for filename in self.filenames:
with open (os.path.join (utils.TEST_ROOT, filename), "w"):
pass |
<|file_name|>test_fs.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from __future__ import unicode_literals
import os, sys
import tempfile
from winsys._compat import unittest
import uuid
import win32file
from winsys.tests.test_fs import utils
from winsys import fs
class TestFS (unittest.TestCase):
filenames = ["%d" % i for i in range (5)]
def setUp (self):
utils.mktemp ()
for filename in self.filenames:
with open (os.path.join (utils.TEST_ROOT, filename), "w"):
pass
def tearDown (self):
<|fim_middle|>
def test_glob (self):
import glob
pattern = os.path.join (utils.TEST_ROOT, "*")
self.assertEquals (list (fs.glob (pattern)), glob.glob (pattern))
def test_listdir (self):
import os
fs_version = list (fs.listdir (utils.TEST_ROOT))
os_version = os.listdir (utils.TEST_ROOT)
self.assertEquals (fs_version, os_version, "%s differs from %s" % (fs_version, os_version))
#
# All the other module-level functions are hand-offs
# to the corresponding Entry methods.
#
if __name__ == "__main__":
unittest.main ()
if sys.stdout.isatty (): raw_input ("Press enter...")
<｜fim▁end｜> | utils.rmtemp () |
<|file_name|>test_fs.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from __future__ import unicode_literals
import os, sys
import tempfile
from winsys._compat import unittest
import uuid
import win32file
from winsys.tests.test_fs import utils
from winsys import fs
class TestFS (unittest.TestCase):
filenames = ["%d" % i for i in range (5)]
def setUp (self):
utils.mktemp ()
for filename in self.filenames:
with open (os.path.join (utils.TEST_ROOT, filename), "w"):
pass
def tearDown (self):
utils.rmtemp ()
def test_glob (self):
<|fim_middle|>
def test_listdir (self):
import os
fs_version = list (fs.listdir (utils.TEST_ROOT))
os_version = os.listdir (utils.TEST_ROOT)
self.assertEquals (fs_version, os_version, "%s differs from %s" % (fs_version, os_version))
#
# All the other module-level functions are hand-offs
# to the corresponding Entry methods.
#
if __name__ == "__main__":
unittest.main ()
if sys.stdout.isatty (): raw_input ("Press enter...")
<｜fim▁end｜> | import glob
pattern = os.path.join (utils.TEST_ROOT, "*")
self.assertEquals (list (fs.glob (pattern)), glob.glob (pattern)) |
<|file_name|>test_fs.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from __future__ import unicode_literals
import os, sys
import tempfile
from winsys._compat import unittest
import uuid
import win32file
from winsys.tests.test_fs import utils
from winsys import fs
class TestFS (unittest.TestCase):
filenames = ["%d" % i for i in range (5)]
def setUp (self):
utils.mktemp ()
for filename in self.filenames:
with open (os.path.join (utils.TEST_ROOT, filename), "w"):
pass
def tearDown (self):
utils.rmtemp ()
def test_glob (self):
import glob
pattern = os.path.join (utils.TEST_ROOT, "*")
self.assertEquals (list (fs.glob (pattern)), glob.glob (pattern))
def test_listdir (self):
<|fim_middle|>
#
# All the other module-level functions are hand-offs
# to the corresponding Entry methods.
#
if __name__ == "__main__":
unittest.main ()
if sys.stdout.isatty (): raw_input ("Press enter...")
<｜fim▁end｜> | import os
fs_version = list (fs.listdir (utils.TEST_ROOT))
os_version = os.listdir (utils.TEST_ROOT)
self.assertEquals (fs_version, os_version, "%s differs from %s" % (fs_version, os_version)) |
<|file_name|>test_fs.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from __future__ import unicode_literals
import os, sys
import tempfile
from winsys._compat import unittest
import uuid
import win32file
from winsys.tests.test_fs import utils
from winsys import fs
class TestFS (unittest.TestCase):
filenames = ["%d" % i for i in range (5)]
def setUp (self):
utils.mktemp ()
for filename in self.filenames:
with open (os.path.join (utils.TEST_ROOT, filename), "w"):
pass
def tearDown (self):
utils.rmtemp ()
def test_glob (self):
import glob
pattern = os.path.join (utils.TEST_ROOT, "*")
self.assertEquals (list (fs.glob (pattern)), glob.glob (pattern))
def test_listdir (self):
import os
fs_version = list (fs.listdir (utils.TEST_ROOT))
os_version = os.listdir (utils.TEST_ROOT)
self.assertEquals (fs_version, os_version, "%s differs from %s" % (fs_version, os_version))
#
# All the other module-level functions are hand-offs
# to the corresponding Entry methods.
#
if __name__ == "__main__":
<|fim_middle|>
<｜fim▁end｜> | unittest.main ()
if sys.stdout.isatty (): raw_input ("Press enter...") |
<|file_name|>test_fs.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from __future__ import unicode_literals
import os, sys
import tempfile
from winsys._compat import unittest
import uuid
import win32file
from winsys.tests.test_fs import utils
from winsys import fs
class TestFS (unittest.TestCase):
filenames = ["%d" % i for i in range (5)]
def setUp (self):
utils.mktemp ()
for filename in self.filenames:
with open (os.path.join (utils.TEST_ROOT, filename), "w"):
pass
def tearDown (self):
utils.rmtemp ()
def test_glob (self):
import glob
pattern = os.path.join (utils.TEST_ROOT, "*")
self.assertEquals (list (fs.glob (pattern)), glob.glob (pattern))
def test_listdir (self):
import os
fs_version = list (fs.listdir (utils.TEST_ROOT))
os_version = os.listdir (utils.TEST_ROOT)
self.assertEquals (fs_version, os_version, "%s differs from %s" % (fs_version, os_version))
#
# All the other module-level functions are hand-offs
# to the corresponding Entry methods.
#
if __name__ == "__main__":
unittest.main ()
if sys.stdout.isatty (): <|fim_middle|>
<｜fim▁end｜> | raw_input ("Press enter...") |
<|file_name|>test_fs.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from __future__ import unicode_literals
import os, sys
import tempfile
from winsys._compat import unittest
import uuid
import win32file
from winsys.tests.test_fs import utils
from winsys import fs
class TestFS (unittest.TestCase):
filenames = ["%d" % i for i in range (5)]
def <|fim_middle|> (self):
utils.mktemp ()
for filename in self.filenames:
with open (os.path.join (utils.TEST_ROOT, filename), "w"):
pass
def tearDown (self):
utils.rmtemp ()
def test_glob (self):
import glob
pattern = os.path.join (utils.TEST_ROOT, "*")
self.assertEquals (list (fs.glob (pattern)), glob.glob (pattern))
def test_listdir (self):
import os
fs_version = list (fs.listdir (utils.TEST_ROOT))
os_version = os.listdir (utils.TEST_ROOT)
self.assertEquals (fs_version, os_version, "%s differs from %s" % (fs_version, os_version))
#
# All the other module-level functions are hand-offs
# to the corresponding Entry methods.
#
if __name__ == "__main__":
unittest.main ()
if sys.stdout.isatty (): raw_input ("Press enter...")
<｜fim▁end｜> | setUp |
<|file_name|>test_fs.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from __future__ import unicode_literals
import os, sys
import tempfile
from winsys._compat import unittest
import uuid
import win32file
from winsys.tests.test_fs import utils
from winsys import fs
class TestFS (unittest.TestCase):
filenames = ["%d" % i for i in range (5)]
def setUp (self):
utils.mktemp ()
for filename in self.filenames:
with open (os.path.join (utils.TEST_ROOT, filename), "w"):
pass
def <|fim_middle|> (self):
utils.rmtemp ()
def test_glob (self):
import glob
pattern = os.path.join (utils.TEST_ROOT, "*")
self.assertEquals (list (fs.glob (pattern)), glob.glob (pattern))
def test_listdir (self):
import os
fs_version = list (fs.listdir (utils.TEST_ROOT))
os_version = os.listdir (utils.TEST_ROOT)
self.assertEquals (fs_version, os_version, "%s differs from %s" % (fs_version, os_version))
#
# All the other module-level functions are hand-offs
# to the corresponding Entry methods.
#
if __name__ == "__main__":
unittest.main ()
if sys.stdout.isatty (): raw_input ("Press enter...")
<｜fim▁end｜> | tearDown |
<|file_name|>test_fs.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from __future__ import unicode_literals
import os, sys
import tempfile
from winsys._compat import unittest
import uuid
import win32file
from winsys.tests.test_fs import utils
from winsys import fs
class TestFS (unittest.TestCase):
filenames = ["%d" % i for i in range (5)]
def setUp (self):
utils.mktemp ()
for filename in self.filenames:
with open (os.path.join (utils.TEST_ROOT, filename), "w"):
pass
def tearDown (self):
utils.rmtemp ()
def <|fim_middle|> (self):
import glob
pattern = os.path.join (utils.TEST_ROOT, "*")
self.assertEquals (list (fs.glob (pattern)), glob.glob (pattern))
def test_listdir (self):
import os
fs_version = list (fs.listdir (utils.TEST_ROOT))
os_version = os.listdir (utils.TEST_ROOT)
self.assertEquals (fs_version, os_version, "%s differs from %s" % (fs_version, os_version))
#
# All the other module-level functions are hand-offs
# to the corresponding Entry methods.
#
if __name__ == "__main__":
unittest.main ()
if sys.stdout.isatty (): raw_input ("Press enter...")
<｜fim▁end｜> | test_glob |
<|file_name|>test_fs.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from __future__ import unicode_literals
import os, sys
import tempfile
from winsys._compat import unittest
import uuid
import win32file
from winsys.tests.test_fs import utils
from winsys import fs
class TestFS (unittest.TestCase):
filenames = ["%d" % i for i in range (5)]
def setUp (self):
utils.mktemp ()
for filename in self.filenames:
with open (os.path.join (utils.TEST_ROOT, filename), "w"):
pass
def tearDown (self):
utils.rmtemp ()
def test_glob (self):
import glob
pattern = os.path.join (utils.TEST_ROOT, "*")
self.assertEquals (list (fs.glob (pattern)), glob.glob (pattern))
def <|fim_middle|> (self):
import os
fs_version = list (fs.listdir (utils.TEST_ROOT))
os_version = os.listdir (utils.TEST_ROOT)
self.assertEquals (fs_version, os_version, "%s differs from %s" % (fs_version, os_version))
#
# All the other module-level functions are hand-offs
# to the corresponding Entry methods.
#
if __name__ == "__main__":
unittest.main ()
if sys.stdout.isatty (): raw_input ("Press enter...")
<｜fim▁end｜> | test_listdir |
<|file_name|>run_evaluation.py<|end_file_name|><｜fim▁begin｜>import os
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)
sys.path.append(os.path.dirname(BASE_DIR))
from global_variables import *
from evaluation_helper import *
cls_names = g_shape_names
img_name_file_list = [os.path.join(g_real_images_voc12val_det_bbox_folder, name+'.txt') for name in cls_names]
det_bbox_mat_file_list = [os.path.join(g_detection_results_folder, x.rstrip()) for x in open(g_rcnn_detection_bbox_mat_filelist)]
result_folder = os.path.join(BASE_DIR, 'avp_test_results')
test_avp_nv(cls_names, img_name_file_list, det_bbox_mat_file_list, result_folder)
img_name_file_list = [os.path.join(g_real_images_voc12val_easy_gt_bbox_folder, name+'.txt') for name in cls_names]
view_label_folder = g_real_images_voc12val_easy_gt_bbox_folder
result_folder = os.path.join(BASE_DIR, 'vp_test_results')<｜fim▁hole｜><｜fim▁end｜> | test_vp_acc(cls_names, img_name_file_list, result_folder, view_label_folder) |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
<｜fim▁hole｜> d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc<｜fim▁end｜> | from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
|
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
<|fim_middle|>
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | """ eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype) |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
<|fim_middle|>
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | """ returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype) |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
<|fim_middle|>
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | return _Ntrapz(y, x, axis=axis) |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
<|fim_middle|>
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | return _Nptp(x, axis) |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
<|fim_middle|>
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | return _Ncumprod(x, axis) |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
<|fim_middle|>
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | return _Nmax(x, axis) |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
<|fim_middle|>
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | return _Nmin(x, axis) |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
<|fim_middle|>
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | return _Nprod(x, axis) |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
<|fim_middle|>
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.)) |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
<|fim_middle|>
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | return _Nmean(x, axis) |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
<|fim_middle|>
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact) |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
<|fim_middle|>
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d)) |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: <|fim_middle|>
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | M = N |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
<|fim_middle|>
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | return m.astype(dtype) |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: <|fim_middle|>
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | M = N |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
<|fim_middle|>
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | return m.astype(dtype) |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
<|fim_middle|>
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | y = m |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
<|fim_middle|>
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | y = y |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
<|fim_middle|>
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | m = transpose(m)
y = transpose(y) |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
<|fim_middle|>
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | m = transpose(m) |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
<|fim_middle|>
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | y = transpose(y) |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
<|fim_middle|>
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | raise ValueError("x and y must have the same number of observations") |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
<|fim_middle|>
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | fact = N*1.0 |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
<|fim_middle|>
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | fact = N-1.0 |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def <|fim_middle|>(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | eye |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def <|fim_middle|>(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | tri |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def <|fim_middle|>(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | trapz |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def <|fim_middle|>(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | ptp |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def <|fim_middle|>(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | cumprod |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def <|fim_middle|>(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | max |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def <|fim_middle|>(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | min |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def <|fim_middle|>(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | prod |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def <|fim_middle|>(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | std |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def <|fim_middle|>(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | mean |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def <|fim_middle|>(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def corrcoef(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | cov |
<|file_name|>mlab.py<|end_file_name|><｜fim▁begin｜># This module is for compatibility only. All functions are defined elsewhere.
__all__ = ['rand', 'tril', 'trapz', 'hanning', 'rot90', 'triu', 'diff', 'angle',
'roots', 'ptp', 'kaiser', 'randn', 'cumprod', 'diag', 'msort',
'LinearAlgebra', 'RandomArray', 'prod', 'std', 'hamming', 'flipud',
'max', 'blackman', 'corrcoef', 'bartlett', 'eye', 'squeeze', 'sinc',
'tri', 'cov', 'svd', 'min', 'median', 'fliplr', 'eig', 'mean']
import numpy.oldnumeric.linear_algebra as LinearAlgebra
import numpy.oldnumeric.random_array as RandomArray
from numpy import tril, trapz as _Ntrapz, hanning, rot90, triu, diff, \
angle, roots, ptp as _Nptp, kaiser, cumprod as _Ncumprod, \
diag, msort, prod as _Nprod, std as _Nstd, hamming, flipud, \
amax as _Nmax, amin as _Nmin, blackman, bartlett, \
squeeze, sinc, median, fliplr, mean as _Nmean, transpose
from numpy.linalg import eig, svd
from numpy.random import rand, randn
import numpy as np
from typeconv import convtypecode
def eye(N, M=None, k=0, typecode=None, dtype=None):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
and everything else is zeros.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def tri(N, M=None, k=0, typecode=None, dtype=None):
""" returns a N-by-M array where all the diagonals starting from
lower left corner up to the k-th are all ones.
"""
dtype = convtypecode(typecode, dtype)
if M is None: M = N
m = np.greater_equal(np.subtract.outer(np.arange(N), np.arange(M)),-k)
if m.dtype != dtype:
return m.astype(dtype)
def trapz(y, x=None, axis=-1):
return _Ntrapz(y, x, axis=axis)
def ptp(x, axis=0):
return _Nptp(x, axis)
def cumprod(x, axis=0):
return _Ncumprod(x, axis)
def max(x, axis=0):
return _Nmax(x, axis)
def min(x, axis=0):
return _Nmin(x, axis)
def prod(x, axis=0):
return _Nprod(x, axis)
def std(x, axis=0):
N = asarray(x).shape[axis]
return _Nstd(x, axis)*sqrt(N/(N-1.))
def mean(x, axis=0):
return _Nmean(x, axis)
# This is exactly the same cov function as in MLab
def cov(m, y=None, rowvar=0, bias=0):
if y is None:
y = m
else:
y = y
if rowvar:
m = transpose(m)
y = transpose(y)
if (m.shape[0] == 1):
m = transpose(m)
if (y.shape[0] == 1):
y = transpose(y)
N = m.shape[0]
if (y.shape[0] != N):
raise ValueError("x and y must have the same number of observations")
m = m - _Nmean(m,axis=0)
y = y - _Nmean(y,axis=0)
if bias:
fact = N*1.0
else:
fact = N-1.0
return squeeze(dot(transpose(m), conjugate(y)) / fact)
from numpy import sqrt, multiply
def <|fim_middle|>(x, y=None):
c = cov(x, y)
d = diag(c)
return c/sqrt(multiply.outer(d,d))
from compat import *
from functions import *
from precision import *
from ufuncs import *
from misc import *
import compat
import precision
import functions
import misc
import ufuncs
import numpy
__version__ = numpy.__version__
del numpy
__all__ += ['__version__']
__all__ += compat.__all__
__all__ += precision.__all__
__all__ += functions.__all__
__all__ += ufuncs.__all__
__all__ += misc.__all__
del compat
del functions
del precision
del ufuncs
del misc
<｜fim▁end｜> | corrcoef |
<|file_name|>checkout.py<|end_file_name|><｜fim▁begin｜># Copyright 2006 John Duda
# This file is part of Infoshopkeeper.
# Infoshopkeeper is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 2 of the
# License, or any later version.
# Infoshopkeeper is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with Infoshopkeeper; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301
# USA
from wxPython.wx import *
import os
import datetime
from objects.emprunt import Emprunt
from popups.members import AddMemberPanel, ShowMembersPanel<｜fim▁hole｜> self.parent=parent
wxDialog.__init__(self, parent,-1,"Check out items")
self.mastersizer = wxBoxSizer(wxVERTICAL)
self.static1 = wxStaticText(self, -1, "Check out to :")
self.mastersizer.Add(self.static1)
self.notebook = wxNotebook(self, -1, style=wxNB_TOP)
self.new_member_panel = AddMemberPanel(parent=self.notebook, main_window=parent,
on_successful_add=self.Borrow, cancel=self.Close)
self.notebook.AddPage(self.new_member_panel, "New member")
self.show_member_panel = ShowMembersPanel(parent=self.notebook, main_window=parent, motherDialog=self, on_select=self.Borrow)
self.notebook.AddPage(self.show_member_panel, "Existing member")
self.mastersizer.Add(self.notebook)
self.SetSizer(self.mastersizer)
for i in self.parent.orderbox.items:
print i.database_id, "... ", i.id
#self.b = wxButton(self, -1, "Checkout", (15, 80))
#EVT_BUTTON(self, self.b.GetId(), self.Checkout)
#self.b.SetDefault()
self.mastersizer.SetSizeHints(self)
def Borrow(self, id):
borrower = self.parent.membersList.get(id)
print borrower
for i in self.parent.orderbox.items:
# Check if this work on sqlobject 0.7... I got
# lots of problem on 0.6.1, and itemID __isn't__
# defined in emprunt, which is plain weirdness
e = Emprunt(borrower = id, itemID=i.database_id)
print i.database_id
self.parent.orderbox.setBorrowed()
self.parent.orderbox.void()
self.Close()
def OnCancel(self,event):
self.EndModal(1)
def Checkout(self,event):
borrower=self.borrower.GetValue()
if len(borrower)>0:
today="%s" % datetime.date.today()
self.parent.orderbox.change_status(today+"-"+borrower)
self.parent.orderbox.void()
self.Close()<｜fim▁end｜> | class CheckoutPopup(wxDialog):
def __init__(self, parent): |
<|file_name|>checkout.py<|end_file_name|><｜fim▁begin｜># Copyright 2006 John Duda
# This file is part of Infoshopkeeper.
# Infoshopkeeper is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 2 of the
# License, or any later version.
# Infoshopkeeper is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with Infoshopkeeper; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301
# USA
from wxPython.wx import *
import os
import datetime
from objects.emprunt import Emprunt
from popups.members import AddMemberPanel, ShowMembersPanel
class CheckoutPopup(wxDialog):
<|fim_middle|>
<｜fim▁end｜> | def __init__(self, parent):
self.parent=parent
wxDialog.__init__(self, parent,-1,"Check out items")
self.mastersizer = wxBoxSizer(wxVERTICAL)
self.static1 = wxStaticText(self, -1, "Check out to :")
self.mastersizer.Add(self.static1)
self.notebook = wxNotebook(self, -1, style=wxNB_TOP)
self.new_member_panel = AddMemberPanel(parent=self.notebook, main_window=parent,
on_successful_add=self.Borrow, cancel=self.Close)
self.notebook.AddPage(self.new_member_panel, "New member")
self.show_member_panel = ShowMembersPanel(parent=self.notebook, main_window=parent, motherDialog=self, on_select=self.Borrow)
self.notebook.AddPage(self.show_member_panel, "Existing member")
self.mastersizer.Add(self.notebook)
self.SetSizer(self.mastersizer)
for i in self.parent.orderbox.items:
print i.database_id, "... ", i.id
#self.b = wxButton(self, -1, "Checkout", (15, 80))
#EVT_BUTTON(self, self.b.GetId(), self.Checkout)
#self.b.SetDefault()
self.mastersizer.SetSizeHints(self)
def Borrow(self, id):
borrower = self.parent.membersList.get(id)
print borrower
for i in self.parent.orderbox.items:
# Check if this work on sqlobject 0.7... I got
# lots of problem on 0.6.1, and itemID __isn't__
# defined in emprunt, which is plain weirdness
e = Emprunt(borrower = id, itemID=i.database_id)
print i.database_id
self.parent.orderbox.setBorrowed()
self.parent.orderbox.void()
self.Close()
def OnCancel(self,event):
self.EndModal(1)
def Checkout(self,event):
borrower=self.borrower.GetValue()
if len(borrower)>0:
today="%s" % datetime.date.today()
self.parent.orderbox.change_status(today+"-"+borrower)
self.parent.orderbox.void()
self.Close() |
<|file_name|>checkout.py<|end_file_name|><｜fim▁begin｜># Copyright 2006 John Duda
# This file is part of Infoshopkeeper.
# Infoshopkeeper is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 2 of the
# License, or any later version.
# Infoshopkeeper is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with Infoshopkeeper; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301
# USA
from wxPython.wx import *
import os
import datetime
from objects.emprunt import Emprunt
from popups.members import AddMemberPanel, ShowMembersPanel
class CheckoutPopup(wxDialog):
def __init__(self, parent):
<|fim_middle|>
def Borrow(self, id):
borrower = self.parent.membersList.get(id)
print borrower
for i in self.parent.orderbox.items:
# Check if this work on sqlobject 0.7... I got
# lots of problem on 0.6.1, and itemID __isn't__
# defined in emprunt, which is plain weirdness
e = Emprunt(borrower = id, itemID=i.database_id)
print i.database_id
self.parent.orderbox.setBorrowed()
self.parent.orderbox.void()
self.Close()
def OnCancel(self,event):
self.EndModal(1)
def Checkout(self,event):
borrower=self.borrower.GetValue()
if len(borrower)>0:
today="%s" % datetime.date.today()
self.parent.orderbox.change_status(today+"-"+borrower)
self.parent.orderbox.void()
self.Close()
<｜fim▁end｜> | self.parent=parent
wxDialog.__init__(self, parent,-1,"Check out items")
self.mastersizer = wxBoxSizer(wxVERTICAL)
self.static1 = wxStaticText(self, -1, "Check out to :")
self.mastersizer.Add(self.static1)
self.notebook = wxNotebook(self, -1, style=wxNB_TOP)
self.new_member_panel = AddMemberPanel(parent=self.notebook, main_window=parent,
on_successful_add=self.Borrow, cancel=self.Close)
self.notebook.AddPage(self.new_member_panel, "New member")
self.show_member_panel = ShowMembersPanel(parent=self.notebook, main_window=parent, motherDialog=self, on_select=self.Borrow)
self.notebook.AddPage(self.show_member_panel, "Existing member")
self.mastersizer.Add(self.notebook)
self.SetSizer(self.mastersizer)
for i in self.parent.orderbox.items:
print i.database_id, "... ", i.id
#self.b = wxButton(self, -1, "Checkout", (15, 80))
#EVT_BUTTON(self, self.b.GetId(), self.Checkout)
#self.b.SetDefault()
self.mastersizer.SetSizeHints(self) |
<|file_name|>checkout.py<|end_file_name|><｜fim▁begin｜># Copyright 2006 John Duda
# This file is part of Infoshopkeeper.
# Infoshopkeeper is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 2 of the
# License, or any later version.
# Infoshopkeeper is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with Infoshopkeeper; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301
# USA
from wxPython.wx import *
import os
import datetime
from objects.emprunt import Emprunt
from popups.members import AddMemberPanel, ShowMembersPanel
class CheckoutPopup(wxDialog):
def __init__(self, parent):
self.parent=parent
wxDialog.__init__(self, parent,-1,"Check out items")
self.mastersizer = wxBoxSizer(wxVERTICAL)
self.static1 = wxStaticText(self, -1, "Check out to :")
self.mastersizer.Add(self.static1)
self.notebook = wxNotebook(self, -1, style=wxNB_TOP)
self.new_member_panel = AddMemberPanel(parent=self.notebook, main_window=parent,
on_successful_add=self.Borrow, cancel=self.Close)
self.notebook.AddPage(self.new_member_panel, "New member")
self.show_member_panel = ShowMembersPanel(parent=self.notebook, main_window=parent, motherDialog=self, on_select=self.Borrow)
self.notebook.AddPage(self.show_member_panel, "Existing member")
self.mastersizer.Add(self.notebook)
self.SetSizer(self.mastersizer)
for i in self.parent.orderbox.items:
print i.database_id, "... ", i.id
#self.b = wxButton(self, -1, "Checkout", (15, 80))
#EVT_BUTTON(self, self.b.GetId(), self.Checkout)
#self.b.SetDefault()
self.mastersizer.SetSizeHints(self)
def Borrow(self, id):
<|fim_middle|>
def OnCancel(self,event):
self.EndModal(1)
def Checkout(self,event):
borrower=self.borrower.GetValue()
if len(borrower)>0:
today="%s" % datetime.date.today()
self.parent.orderbox.change_status(today+"-"+borrower)
self.parent.orderbox.void()
self.Close()
<｜fim▁end｜> | borrower = self.parent.membersList.get(id)
print borrower
for i in self.parent.orderbox.items:
# Check if this work on sqlobject 0.7... I got
# lots of problem on 0.6.1, and itemID __isn't__
# defined in emprunt, which is plain weirdness
e = Emprunt(borrower = id, itemID=i.database_id)
print i.database_id
self.parent.orderbox.setBorrowed()
self.parent.orderbox.void()
self.Close() |
<|file_name|>checkout.py<|end_file_name|><｜fim▁begin｜># Copyright 2006 John Duda
# This file is part of Infoshopkeeper.
# Infoshopkeeper is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 2 of the
# License, or any later version.
# Infoshopkeeper is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with Infoshopkeeper; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301
# USA
from wxPython.wx import *
import os
import datetime
from objects.emprunt import Emprunt
from popups.members import AddMemberPanel, ShowMembersPanel
class CheckoutPopup(wxDialog):
def __init__(self, parent):
self.parent=parent
wxDialog.__init__(self, parent,-1,"Check out items")
self.mastersizer = wxBoxSizer(wxVERTICAL)
self.static1 = wxStaticText(self, -1, "Check out to :")
self.mastersizer.Add(self.static1)
self.notebook = wxNotebook(self, -1, style=wxNB_TOP)
self.new_member_panel = AddMemberPanel(parent=self.notebook, main_window=parent,
on_successful_add=self.Borrow, cancel=self.Close)
self.notebook.AddPage(self.new_member_panel, "New member")
self.show_member_panel = ShowMembersPanel(parent=self.notebook, main_window=parent, motherDialog=self, on_select=self.Borrow)
self.notebook.AddPage(self.show_member_panel, "Existing member")
self.mastersizer.Add(self.notebook)
self.SetSizer(self.mastersizer)
for i in self.parent.orderbox.items:
print i.database_id, "... ", i.id
#self.b = wxButton(self, -1, "Checkout", (15, 80))
#EVT_BUTTON(self, self.b.GetId(), self.Checkout)
#self.b.SetDefault()
self.mastersizer.SetSizeHints(self)
def Borrow(self, id):
borrower = self.parent.membersList.get(id)
print borrower
for i in self.parent.orderbox.items:
# Check if this work on sqlobject 0.7... I got
# lots of problem on 0.6.1, and itemID __isn't__
# defined in emprunt, which is plain weirdness
e = Emprunt(borrower = id, itemID=i.database_id)
print i.database_id
self.parent.orderbox.setBorrowed()
self.parent.orderbox.void()
self.Close()
def OnCancel(self,event):
<|fim_middle|>
def Checkout(self,event):
borrower=self.borrower.GetValue()
if len(borrower)>0:
today="%s" % datetime.date.today()
self.parent.orderbox.change_status(today+"-"+borrower)
self.parent.orderbox.void()
self.Close()
<｜fim▁end｜> | self.EndModal(1) |
<|file_name|>checkout.py<|end_file_name|><｜fim▁begin｜># Copyright 2006 John Duda
# This file is part of Infoshopkeeper.
# Infoshopkeeper is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 2 of the
# License, or any later version.
# Infoshopkeeper is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with Infoshopkeeper; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301
# USA
from wxPython.wx import *
import os
import datetime
from objects.emprunt import Emprunt
from popups.members import AddMemberPanel, ShowMembersPanel
class CheckoutPopup(wxDialog):
def __init__(self, parent):
self.parent=parent
wxDialog.__init__(self, parent,-1,"Check out items")
self.mastersizer = wxBoxSizer(wxVERTICAL)
self.static1 = wxStaticText(self, -1, "Check out to :")
self.mastersizer.Add(self.static1)
self.notebook = wxNotebook(self, -1, style=wxNB_TOP)
self.new_member_panel = AddMemberPanel(parent=self.notebook, main_window=parent,
on_successful_add=self.Borrow, cancel=self.Close)
self.notebook.AddPage(self.new_member_panel, "New member")
self.show_member_panel = ShowMembersPanel(parent=self.notebook, main_window=parent, motherDialog=self, on_select=self.Borrow)
self.notebook.AddPage(self.show_member_panel, "Existing member")
self.mastersizer.Add(self.notebook)
self.SetSizer(self.mastersizer)
for i in self.parent.orderbox.items:
print i.database_id, "... ", i.id
#self.b = wxButton(self, -1, "Checkout", (15, 80))
#EVT_BUTTON(self, self.b.GetId(), self.Checkout)
#self.b.SetDefault()
self.mastersizer.SetSizeHints(self)
def Borrow(self, id):
borrower = self.parent.membersList.get(id)
print borrower
for i in self.parent.orderbox.items:
# Check if this work on sqlobject 0.7... I got
# lots of problem on 0.6.1, and itemID __isn't__
# defined in emprunt, which is plain weirdness
e = Emprunt(borrower = id, itemID=i.database_id)
print i.database_id
self.parent.orderbox.setBorrowed()
self.parent.orderbox.void()
self.Close()
def OnCancel(self,event):
self.EndModal(1)
def Checkout(self,event):
<|fim_middle|>
<｜fim▁end｜> | borrower=self.borrower.GetValue()
if len(borrower)>0:
today="%s" % datetime.date.today()
self.parent.orderbox.change_status(today+"-"+borrower)
self.parent.orderbox.void()
self.Close() |
<|file_name|>checkout.py<|end_file_name|><｜fim▁begin｜># Copyright 2006 John Duda
# This file is part of Infoshopkeeper.
# Infoshopkeeper is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 2 of the
# License, or any later version.
# Infoshopkeeper is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with Infoshopkeeper; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301
# USA
from wxPython.wx import *
import os
import datetime
from objects.emprunt import Emprunt
from popups.members import AddMemberPanel, ShowMembersPanel
class CheckoutPopup(wxDialog):
def __init__(self, parent):
self.parent=parent
wxDialog.__init__(self, parent,-1,"Check out items")
self.mastersizer = wxBoxSizer(wxVERTICAL)
self.static1 = wxStaticText(self, -1, "Check out to :")
self.mastersizer.Add(self.static1)
self.notebook = wxNotebook(self, -1, style=wxNB_TOP)
self.new_member_panel = AddMemberPanel(parent=self.notebook, main_window=parent,
on_successful_add=self.Borrow, cancel=self.Close)
self.notebook.AddPage(self.new_member_panel, "New member")
self.show_member_panel = ShowMembersPanel(parent=self.notebook, main_window=parent, motherDialog=self, on_select=self.Borrow)
self.notebook.AddPage(self.show_member_panel, "Existing member")
self.mastersizer.Add(self.notebook)
self.SetSizer(self.mastersizer)
for i in self.parent.orderbox.items:
print i.database_id, "... ", i.id
#self.b = wxButton(self, -1, "Checkout", (15, 80))
#EVT_BUTTON(self, self.b.GetId(), self.Checkout)
#self.b.SetDefault()
self.mastersizer.SetSizeHints(self)
def Borrow(self, id):
borrower = self.parent.membersList.get(id)
print borrower
for i in self.parent.orderbox.items:
# Check if this work on sqlobject 0.7... I got
# lots of problem on 0.6.1, and itemID __isn't__
# defined in emprunt, which is plain weirdness
e = Emprunt(borrower = id, itemID=i.database_id)
print i.database_id
self.parent.orderbox.setBorrowed()
self.parent.orderbox.void()
self.Close()
def OnCancel(self,event):
self.EndModal(1)
def Checkout(self,event):
borrower=self.borrower.GetValue()
if len(borrower)>0:
<|fim_middle|>
self.Close()
<｜fim▁end｜> | today="%s" % datetime.date.today()
self.parent.orderbox.change_status(today+"-"+borrower)
self.parent.orderbox.void() |
<|file_name|>checkout.py<|end_file_name|><｜fim▁begin｜># Copyright 2006 John Duda
# This file is part of Infoshopkeeper.
# Infoshopkeeper is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 2 of the
# License, or any later version.
# Infoshopkeeper is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with Infoshopkeeper; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301
# USA
from wxPython.wx import *
import os
import datetime
from objects.emprunt import Emprunt
from popups.members import AddMemberPanel, ShowMembersPanel
class CheckoutPopup(wxDialog):
def <|fim_middle|>(self, parent):
self.parent=parent
wxDialog.__init__(self, parent,-1,"Check out items")
self.mastersizer = wxBoxSizer(wxVERTICAL)
self.static1 = wxStaticText(self, -1, "Check out to :")
self.mastersizer.Add(self.static1)
self.notebook = wxNotebook(self, -1, style=wxNB_TOP)
self.new_member_panel = AddMemberPanel(parent=self.notebook, main_window=parent,
on_successful_add=self.Borrow, cancel=self.Close)
self.notebook.AddPage(self.new_member_panel, "New member")
self.show_member_panel = ShowMembersPanel(parent=self.notebook, main_window=parent, motherDialog=self, on_select=self.Borrow)
self.notebook.AddPage(self.show_member_panel, "Existing member")
self.mastersizer.Add(self.notebook)
self.SetSizer(self.mastersizer)
for i in self.parent.orderbox.items:
print i.database_id, "... ", i.id
#self.b = wxButton(self, -1, "Checkout", (15, 80))
#EVT_BUTTON(self, self.b.GetId(), self.Checkout)
#self.b.SetDefault()
self.mastersizer.SetSizeHints(self)
def Borrow(self, id):
borrower = self.parent.membersList.get(id)
print borrower
for i in self.parent.orderbox.items:
# Check if this work on sqlobject 0.7... I got
# lots of problem on 0.6.1, and itemID __isn't__
# defined in emprunt, which is plain weirdness
e = Emprunt(borrower = id, itemID=i.database_id)
print i.database_id
self.parent.orderbox.setBorrowed()
self.parent.orderbox.void()
self.Close()
def OnCancel(self,event):
self.EndModal(1)
def Checkout(self,event):
borrower=self.borrower.GetValue()
if len(borrower)>0:
today="%s" % datetime.date.today()
self.parent.orderbox.change_status(today+"-"+borrower)
self.parent.orderbox.void()
self.Close()
<｜fim▁end｜> | __init__ |
<|file_name|>checkout.py<|end_file_name|><｜fim▁begin｜># Copyright 2006 John Duda
# This file is part of Infoshopkeeper.
# Infoshopkeeper is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 2 of the
# License, or any later version.
# Infoshopkeeper is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with Infoshopkeeper; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301
# USA
from wxPython.wx import *
import os
import datetime
from objects.emprunt import Emprunt
from popups.members import AddMemberPanel, ShowMembersPanel
class CheckoutPopup(wxDialog):
def __init__(self, parent):
self.parent=parent
wxDialog.__init__(self, parent,-1,"Check out items")
self.mastersizer = wxBoxSizer(wxVERTICAL)
self.static1 = wxStaticText(self, -1, "Check out to :")
self.mastersizer.Add(self.static1)
self.notebook = wxNotebook(self, -1, style=wxNB_TOP)
self.new_member_panel = AddMemberPanel(parent=self.notebook, main_window=parent,
on_successful_add=self.Borrow, cancel=self.Close)
self.notebook.AddPage(self.new_member_panel, "New member")
self.show_member_panel = ShowMembersPanel(parent=self.notebook, main_window=parent, motherDialog=self, on_select=self.Borrow)
self.notebook.AddPage(self.show_member_panel, "Existing member")
self.mastersizer.Add(self.notebook)
self.SetSizer(self.mastersizer)
for i in self.parent.orderbox.items:
print i.database_id, "... ", i.id
#self.b = wxButton(self, -1, "Checkout", (15, 80))
#EVT_BUTTON(self, self.b.GetId(), self.Checkout)
#self.b.SetDefault()
self.mastersizer.SetSizeHints(self)
def <|fim_middle|>(self, id):
borrower = self.parent.membersList.get(id)
print borrower
for i in self.parent.orderbox.items:
# Check if this work on sqlobject 0.7... I got
# lots of problem on 0.6.1, and itemID __isn't__
# defined in emprunt, which is plain weirdness
e = Emprunt(borrower = id, itemID=i.database_id)
print i.database_id
self.parent.orderbox.setBorrowed()
self.parent.orderbox.void()
self.Close()
def OnCancel(self,event):
self.EndModal(1)
def Checkout(self,event):
borrower=self.borrower.GetValue()
if len(borrower)>0:
today="%s" % datetime.date.today()
self.parent.orderbox.change_status(today+"-"+borrower)
self.parent.orderbox.void()
self.Close()
<｜fim▁end｜> | Borrow |
<|file_name|>checkout.py<|end_file_name|><｜fim▁begin｜># Copyright 2006 John Duda
# This file is part of Infoshopkeeper.
# Infoshopkeeper is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 2 of the
# License, or any later version.
# Infoshopkeeper is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with Infoshopkeeper; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301
# USA
from wxPython.wx import *
import os
import datetime
from objects.emprunt import Emprunt
from popups.members import AddMemberPanel, ShowMembersPanel
class CheckoutPopup(wxDialog):
def __init__(self, parent):
self.parent=parent
wxDialog.__init__(self, parent,-1,"Check out items")
self.mastersizer = wxBoxSizer(wxVERTICAL)
self.static1 = wxStaticText(self, -1, "Check out to :")
self.mastersizer.Add(self.static1)
self.notebook = wxNotebook(self, -1, style=wxNB_TOP)
self.new_member_panel = AddMemberPanel(parent=self.notebook, main_window=parent,
on_successful_add=self.Borrow, cancel=self.Close)
self.notebook.AddPage(self.new_member_panel, "New member")
self.show_member_panel = ShowMembersPanel(parent=self.notebook, main_window=parent, motherDialog=self, on_select=self.Borrow)
self.notebook.AddPage(self.show_member_panel, "Existing member")
self.mastersizer.Add(self.notebook)
self.SetSizer(self.mastersizer)
for i in self.parent.orderbox.items:
print i.database_id, "... ", i.id
#self.b = wxButton(self, -1, "Checkout", (15, 80))
#EVT_BUTTON(self, self.b.GetId(), self.Checkout)
#self.b.SetDefault()
self.mastersizer.SetSizeHints(self)
def Borrow(self, id):
borrower = self.parent.membersList.get(id)
print borrower
for i in self.parent.orderbox.items:
# Check if this work on sqlobject 0.7... I got
# lots of problem on 0.6.1, and itemID __isn't__
# defined in emprunt, which is plain weirdness
e = Emprunt(borrower = id, itemID=i.database_id)
print i.database_id
self.parent.orderbox.setBorrowed()
self.parent.orderbox.void()
self.Close()
def <|fim_middle|>(self,event):
self.EndModal(1)
def Checkout(self,event):
borrower=self.borrower.GetValue()
if len(borrower)>0:
today="%s" % datetime.date.today()
self.parent.orderbox.change_status(today+"-"+borrower)
self.parent.orderbox.void()
self.Close()
<｜fim▁end｜> | OnCancel |
<|file_name|>checkout.py<|end_file_name|><｜fim▁begin｜># Copyright 2006 John Duda
# This file is part of Infoshopkeeper.
# Infoshopkeeper is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 2 of the
# License, or any later version.
# Infoshopkeeper is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with Infoshopkeeper; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301
# USA
from wxPython.wx import *
import os
import datetime
from objects.emprunt import Emprunt
from popups.members import AddMemberPanel, ShowMembersPanel
class CheckoutPopup(wxDialog):
def __init__(self, parent):
self.parent=parent
wxDialog.__init__(self, parent,-1,"Check out items")
self.mastersizer = wxBoxSizer(wxVERTICAL)
self.static1 = wxStaticText(self, -1, "Check out to :")
self.mastersizer.Add(self.static1)
self.notebook = wxNotebook(self, -1, style=wxNB_TOP)
self.new_member_panel = AddMemberPanel(parent=self.notebook, main_window=parent,
on_successful_add=self.Borrow, cancel=self.Close)
self.notebook.AddPage(self.new_member_panel, "New member")
self.show_member_panel = ShowMembersPanel(parent=self.notebook, main_window=parent, motherDialog=self, on_select=self.Borrow)
self.notebook.AddPage(self.show_member_panel, "Existing member")
self.mastersizer.Add(self.notebook)
self.SetSizer(self.mastersizer)
for i in self.parent.orderbox.items:
print i.database_id, "... ", i.id
#self.b = wxButton(self, -1, "Checkout", (15, 80))
#EVT_BUTTON(self, self.b.GetId(), self.Checkout)
#self.b.SetDefault()
self.mastersizer.SetSizeHints(self)
def Borrow(self, id):
borrower = self.parent.membersList.get(id)
print borrower
for i in self.parent.orderbox.items:
# Check if this work on sqlobject 0.7... I got
# lots of problem on 0.6.1, and itemID __isn't__
# defined in emprunt, which is plain weirdness
e = Emprunt(borrower = id, itemID=i.database_id)
print i.database_id
self.parent.orderbox.setBorrowed()
self.parent.orderbox.void()
self.Close()
def OnCancel(self,event):
self.EndModal(1)
def <|fim_middle|>(self,event):
borrower=self.borrower.GetValue()
if len(borrower)>0:
today="%s" % datetime.date.today()
self.parent.orderbox.change_status(today+"-"+borrower)
self.parent.orderbox.void()
self.Close()
<｜fim▁end｜> | Checkout |
<|file_name|>buildhistory.py<|end_file_name|><｜fim▁begin｜>import os
import unittest
import tempfile
from git import Repo
from oeqa.utils.commands import get_bb_var
from oe.buildhistory_analysis import blob_to_dict, compare_dict_blobs
class TestBlobParsing(unittest.TestCase):
def setUp(self):
import time
self.repo_path = tempfile.mkdtemp(prefix='selftest-buildhistory',
dir=get_bb_var('TOPDIR'))
self.repo = Repo.init(self.repo_path)
self.test_file = "test"<｜fim▁hole｜> self.var_map = {}
def tearDown(self):
import shutil
shutil.rmtree(self.repo_path)
def commit_vars(self, to_add={}, to_remove = [], msg="A commit message"):
if len(to_add) == 0 and len(to_remove) == 0:
return
for k in to_remove:
self.var_map.pop(x,None)
for k in to_add:
self.var_map[k] = to_add[k]
with open(os.path.join(self.repo_path, self.test_file), 'w') as repo_file:
for k in self.var_map:
repo_file.write("%s = %s\n" % (k, self.var_map[k]))
self.repo.git.add("--all")
self.repo.git.commit(message=msg)
def test_blob_to_dict(self):
"""
Test convertion of git blobs to dictionary
"""
valuesmap = { "foo" : "1", "bar" : "2" }
self.commit_vars(to_add = valuesmap)
blob = self.repo.head.commit.tree.blobs[0]
self.assertEqual(valuesmap, blob_to_dict(blob),
"commit was not translated correctly to dictionary")
def test_compare_dict_blobs(self):
"""
Test comparisson of dictionaries extracted from git blobs
"""
changesmap = { "foo-2" : ("2", "8"), "bar" : ("","4"), "bar-2" : ("","5")}
self.commit_vars(to_add = { "foo" : "1", "foo-2" : "2", "foo-3" : "3" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "foo-2" : "8", "bar" : "4", "bar-2" : "5" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = { x.fieldname : (x.oldvalue, x.newvalue) for x in change_records}
self.assertEqual(changesmap, var_changes, "Changes not reported correctly")
def test_compare_dict_blobs_default(self):
"""
Test default values for comparisson of git blob dictionaries
"""
defaultmap = { x : ("default", "1") for x in ["PKG", "PKGE", "PKGV", "PKGR"]}
self.commit_vars(to_add = { "foo" : "1" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "PKG" : "1", "PKGE" : "1", "PKGV" : "1", "PKGR" : "1" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = {}
for x in change_records:
oldvalue = "default" if ("default" in x.oldvalue) else x.oldvalue
var_changes[x.fieldname] = (oldvalue, x.newvalue)
self.assertEqual(defaultmap, var_changes, "Defaults not set properly")<｜fim▁end｜> | |
<|file_name|>buildhistory.py<|end_file_name|><｜fim▁begin｜>import os
import unittest
import tempfile
from git import Repo
from oeqa.utils.commands import get_bb_var
from oe.buildhistory_analysis import blob_to_dict, compare_dict_blobs
class TestBlobParsing(unittest.TestCase):
<|fim_middle|>
<｜fim▁end｜> | def setUp(self):
import time
self.repo_path = tempfile.mkdtemp(prefix='selftest-buildhistory',
dir=get_bb_var('TOPDIR'))
self.repo = Repo.init(self.repo_path)
self.test_file = "test"
self.var_map = {}
def tearDown(self):
import shutil
shutil.rmtree(self.repo_path)
def commit_vars(self, to_add={}, to_remove = [], msg="A commit message"):
if len(to_add) == 0 and len(to_remove) == 0:
return
for k in to_remove:
self.var_map.pop(x,None)
for k in to_add:
self.var_map[k] = to_add[k]
with open(os.path.join(self.repo_path, self.test_file), 'w') as repo_file:
for k in self.var_map:
repo_file.write("%s = %s\n" % (k, self.var_map[k]))
self.repo.git.add("--all")
self.repo.git.commit(message=msg)
def test_blob_to_dict(self):
"""
Test convertion of git blobs to dictionary
"""
valuesmap = { "foo" : "1", "bar" : "2" }
self.commit_vars(to_add = valuesmap)
blob = self.repo.head.commit.tree.blobs[0]
self.assertEqual(valuesmap, blob_to_dict(blob),
"commit was not translated correctly to dictionary")
def test_compare_dict_blobs(self):
"""
Test comparisson of dictionaries extracted from git blobs
"""
changesmap = { "foo-2" : ("2", "8"), "bar" : ("","4"), "bar-2" : ("","5")}
self.commit_vars(to_add = { "foo" : "1", "foo-2" : "2", "foo-3" : "3" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "foo-2" : "8", "bar" : "4", "bar-2" : "5" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = { x.fieldname : (x.oldvalue, x.newvalue) for x in change_records}
self.assertEqual(changesmap, var_changes, "Changes not reported correctly")
def test_compare_dict_blobs_default(self):
"""
Test default values for comparisson of git blob dictionaries
"""
defaultmap = { x : ("default", "1") for x in ["PKG", "PKGE", "PKGV", "PKGR"]}
self.commit_vars(to_add = { "foo" : "1" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "PKG" : "1", "PKGE" : "1", "PKGV" : "1", "PKGR" : "1" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = {}
for x in change_records:
oldvalue = "default" if ("default" in x.oldvalue) else x.oldvalue
var_changes[x.fieldname] = (oldvalue, x.newvalue)
self.assertEqual(defaultmap, var_changes, "Defaults not set properly") |
<|file_name|>buildhistory.py<|end_file_name|><｜fim▁begin｜>import os
import unittest
import tempfile
from git import Repo
from oeqa.utils.commands import get_bb_var
from oe.buildhistory_analysis import blob_to_dict, compare_dict_blobs
class TestBlobParsing(unittest.TestCase):
def setUp(self):
<|fim_middle|>
def tearDown(self):
import shutil
shutil.rmtree(self.repo_path)
def commit_vars(self, to_add={}, to_remove = [], msg="A commit message"):
if len(to_add) == 0 and len(to_remove) == 0:
return
for k in to_remove:
self.var_map.pop(x,None)
for k in to_add:
self.var_map[k] = to_add[k]
with open(os.path.join(self.repo_path, self.test_file), 'w') as repo_file:
for k in self.var_map:
repo_file.write("%s = %s\n" % (k, self.var_map[k]))
self.repo.git.add("--all")
self.repo.git.commit(message=msg)
def test_blob_to_dict(self):
"""
Test convertion of git blobs to dictionary
"""
valuesmap = { "foo" : "1", "bar" : "2" }
self.commit_vars(to_add = valuesmap)
blob = self.repo.head.commit.tree.blobs[0]
self.assertEqual(valuesmap, blob_to_dict(blob),
"commit was not translated correctly to dictionary")
def test_compare_dict_blobs(self):
"""
Test comparisson of dictionaries extracted from git blobs
"""
changesmap = { "foo-2" : ("2", "8"), "bar" : ("","4"), "bar-2" : ("","5")}
self.commit_vars(to_add = { "foo" : "1", "foo-2" : "2", "foo-3" : "3" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "foo-2" : "8", "bar" : "4", "bar-2" : "5" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = { x.fieldname : (x.oldvalue, x.newvalue) for x in change_records}
self.assertEqual(changesmap, var_changes, "Changes not reported correctly")
def test_compare_dict_blobs_default(self):
"""
Test default values for comparisson of git blob dictionaries
"""
defaultmap = { x : ("default", "1") for x in ["PKG", "PKGE", "PKGV", "PKGR"]}
self.commit_vars(to_add = { "foo" : "1" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "PKG" : "1", "PKGE" : "1", "PKGV" : "1", "PKGR" : "1" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = {}
for x in change_records:
oldvalue = "default" if ("default" in x.oldvalue) else x.oldvalue
var_changes[x.fieldname] = (oldvalue, x.newvalue)
self.assertEqual(defaultmap, var_changes, "Defaults not set properly")
<｜fim▁end｜> | import time
self.repo_path = tempfile.mkdtemp(prefix='selftest-buildhistory',
dir=get_bb_var('TOPDIR'))
self.repo = Repo.init(self.repo_path)
self.test_file = "test"
self.var_map = {} |
<|file_name|>buildhistory.py<|end_file_name|><｜fim▁begin｜>import os
import unittest
import tempfile
from git import Repo
from oeqa.utils.commands import get_bb_var
from oe.buildhistory_analysis import blob_to_dict, compare_dict_blobs
class TestBlobParsing(unittest.TestCase):
def setUp(self):
import time
self.repo_path = tempfile.mkdtemp(prefix='selftest-buildhistory',
dir=get_bb_var('TOPDIR'))
self.repo = Repo.init(self.repo_path)
self.test_file = "test"
self.var_map = {}
def tearDown(self):
<|fim_middle|>
def commit_vars(self, to_add={}, to_remove = [], msg="A commit message"):
if len(to_add) == 0 and len(to_remove) == 0:
return
for k in to_remove:
self.var_map.pop(x,None)
for k in to_add:
self.var_map[k] = to_add[k]
with open(os.path.join(self.repo_path, self.test_file), 'w') as repo_file:
for k in self.var_map:
repo_file.write("%s = %s\n" % (k, self.var_map[k]))
self.repo.git.add("--all")
self.repo.git.commit(message=msg)
def test_blob_to_dict(self):
"""
Test convertion of git blobs to dictionary
"""
valuesmap = { "foo" : "1", "bar" : "2" }
self.commit_vars(to_add = valuesmap)
blob = self.repo.head.commit.tree.blobs[0]
self.assertEqual(valuesmap, blob_to_dict(blob),
"commit was not translated correctly to dictionary")
def test_compare_dict_blobs(self):
"""
Test comparisson of dictionaries extracted from git blobs
"""
changesmap = { "foo-2" : ("2", "8"), "bar" : ("","4"), "bar-2" : ("","5")}
self.commit_vars(to_add = { "foo" : "1", "foo-2" : "2", "foo-3" : "3" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "foo-2" : "8", "bar" : "4", "bar-2" : "5" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = { x.fieldname : (x.oldvalue, x.newvalue) for x in change_records}
self.assertEqual(changesmap, var_changes, "Changes not reported correctly")
def test_compare_dict_blobs_default(self):
"""
Test default values for comparisson of git blob dictionaries
"""
defaultmap = { x : ("default", "1") for x in ["PKG", "PKGE", "PKGV", "PKGR"]}
self.commit_vars(to_add = { "foo" : "1" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "PKG" : "1", "PKGE" : "1", "PKGV" : "1", "PKGR" : "1" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = {}
for x in change_records:
oldvalue = "default" if ("default" in x.oldvalue) else x.oldvalue
var_changes[x.fieldname] = (oldvalue, x.newvalue)
self.assertEqual(defaultmap, var_changes, "Defaults not set properly")
<｜fim▁end｜> | import shutil
shutil.rmtree(self.repo_path) |
<|file_name|>buildhistory.py<|end_file_name|><｜fim▁begin｜>import os
import unittest
import tempfile
from git import Repo
from oeqa.utils.commands import get_bb_var
from oe.buildhistory_analysis import blob_to_dict, compare_dict_blobs
class TestBlobParsing(unittest.TestCase):
def setUp(self):
import time
self.repo_path = tempfile.mkdtemp(prefix='selftest-buildhistory',
dir=get_bb_var('TOPDIR'))
self.repo = Repo.init(self.repo_path)
self.test_file = "test"
self.var_map = {}
def tearDown(self):
import shutil
shutil.rmtree(self.repo_path)
def commit_vars(self, to_add={}, to_remove = [], msg="A commit message"):
<|fim_middle|>
def test_blob_to_dict(self):
"""
Test convertion of git blobs to dictionary
"""
valuesmap = { "foo" : "1", "bar" : "2" }
self.commit_vars(to_add = valuesmap)
blob = self.repo.head.commit.tree.blobs[0]
self.assertEqual(valuesmap, blob_to_dict(blob),
"commit was not translated correctly to dictionary")
def test_compare_dict_blobs(self):
"""
Test comparisson of dictionaries extracted from git blobs
"""
changesmap = { "foo-2" : ("2", "8"), "bar" : ("","4"), "bar-2" : ("","5")}
self.commit_vars(to_add = { "foo" : "1", "foo-2" : "2", "foo-3" : "3" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "foo-2" : "8", "bar" : "4", "bar-2" : "5" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = { x.fieldname : (x.oldvalue, x.newvalue) for x in change_records}
self.assertEqual(changesmap, var_changes, "Changes not reported correctly")
def test_compare_dict_blobs_default(self):
"""
Test default values for comparisson of git blob dictionaries
"""
defaultmap = { x : ("default", "1") for x in ["PKG", "PKGE", "PKGV", "PKGR"]}
self.commit_vars(to_add = { "foo" : "1" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "PKG" : "1", "PKGE" : "1", "PKGV" : "1", "PKGR" : "1" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = {}
for x in change_records:
oldvalue = "default" if ("default" in x.oldvalue) else x.oldvalue
var_changes[x.fieldname] = (oldvalue, x.newvalue)
self.assertEqual(defaultmap, var_changes, "Defaults not set properly")
<｜fim▁end｜> | if len(to_add) == 0 and len(to_remove) == 0:
return
for k in to_remove:
self.var_map.pop(x,None)
for k in to_add:
self.var_map[k] = to_add[k]
with open(os.path.join(self.repo_path, self.test_file), 'w') as repo_file:
for k in self.var_map:
repo_file.write("%s = %s\n" % (k, self.var_map[k]))
self.repo.git.add("--all")
self.repo.git.commit(message=msg) |
<|file_name|>buildhistory.py<|end_file_name|><｜fim▁begin｜>import os
import unittest
import tempfile
from git import Repo
from oeqa.utils.commands import get_bb_var
from oe.buildhistory_analysis import blob_to_dict, compare_dict_blobs
class TestBlobParsing(unittest.TestCase):
def setUp(self):
import time
self.repo_path = tempfile.mkdtemp(prefix='selftest-buildhistory',
dir=get_bb_var('TOPDIR'))
self.repo = Repo.init(self.repo_path)
self.test_file = "test"
self.var_map = {}
def tearDown(self):
import shutil
shutil.rmtree(self.repo_path)
def commit_vars(self, to_add={}, to_remove = [], msg="A commit message"):
if len(to_add) == 0 and len(to_remove) == 0:
return
for k in to_remove:
self.var_map.pop(x,None)
for k in to_add:
self.var_map[k] = to_add[k]
with open(os.path.join(self.repo_path, self.test_file), 'w') as repo_file:
for k in self.var_map:
repo_file.write("%s = %s\n" % (k, self.var_map[k]))
self.repo.git.add("--all")
self.repo.git.commit(message=msg)
def test_blob_to_dict(self):
<|fim_middle|>
def test_compare_dict_blobs(self):
"""
Test comparisson of dictionaries extracted from git blobs
"""
changesmap = { "foo-2" : ("2", "8"), "bar" : ("","4"), "bar-2" : ("","5")}
self.commit_vars(to_add = { "foo" : "1", "foo-2" : "2", "foo-3" : "3" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "foo-2" : "8", "bar" : "4", "bar-2" : "5" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = { x.fieldname : (x.oldvalue, x.newvalue) for x in change_records}
self.assertEqual(changesmap, var_changes, "Changes not reported correctly")
def test_compare_dict_blobs_default(self):
"""
Test default values for comparisson of git blob dictionaries
"""
defaultmap = { x : ("default", "1") for x in ["PKG", "PKGE", "PKGV", "PKGR"]}
self.commit_vars(to_add = { "foo" : "1" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "PKG" : "1", "PKGE" : "1", "PKGV" : "1", "PKGR" : "1" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = {}
for x in change_records:
oldvalue = "default" if ("default" in x.oldvalue) else x.oldvalue
var_changes[x.fieldname] = (oldvalue, x.newvalue)
self.assertEqual(defaultmap, var_changes, "Defaults not set properly")
<｜fim▁end｜> | """
Test convertion of git blobs to dictionary
"""
valuesmap = { "foo" : "1", "bar" : "2" }
self.commit_vars(to_add = valuesmap)
blob = self.repo.head.commit.tree.blobs[0]
self.assertEqual(valuesmap, blob_to_dict(blob),
"commit was not translated correctly to dictionary") |
<|file_name|>buildhistory.py<|end_file_name|><｜fim▁begin｜>import os
import unittest
import tempfile
from git import Repo
from oeqa.utils.commands import get_bb_var
from oe.buildhistory_analysis import blob_to_dict, compare_dict_blobs
class TestBlobParsing(unittest.TestCase):
def setUp(self):
import time
self.repo_path = tempfile.mkdtemp(prefix='selftest-buildhistory',
dir=get_bb_var('TOPDIR'))
self.repo = Repo.init(self.repo_path)
self.test_file = "test"
self.var_map = {}
def tearDown(self):
import shutil
shutil.rmtree(self.repo_path)
def commit_vars(self, to_add={}, to_remove = [], msg="A commit message"):
if len(to_add) == 0 and len(to_remove) == 0:
return
for k in to_remove:
self.var_map.pop(x,None)
for k in to_add:
self.var_map[k] = to_add[k]
with open(os.path.join(self.repo_path, self.test_file), 'w') as repo_file:
for k in self.var_map:
repo_file.write("%s = %s\n" % (k, self.var_map[k]))
self.repo.git.add("--all")
self.repo.git.commit(message=msg)
def test_blob_to_dict(self):
"""
Test convertion of git blobs to dictionary
"""
valuesmap = { "foo" : "1", "bar" : "2" }
self.commit_vars(to_add = valuesmap)
blob = self.repo.head.commit.tree.blobs[0]
self.assertEqual(valuesmap, blob_to_dict(blob),
"commit was not translated correctly to dictionary")
def test_compare_dict_blobs(self):
<|fim_middle|>
def test_compare_dict_blobs_default(self):
"""
Test default values for comparisson of git blob dictionaries
"""
defaultmap = { x : ("default", "1") for x in ["PKG", "PKGE", "PKGV", "PKGR"]}
self.commit_vars(to_add = { "foo" : "1" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "PKG" : "1", "PKGE" : "1", "PKGV" : "1", "PKGR" : "1" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = {}
for x in change_records:
oldvalue = "default" if ("default" in x.oldvalue) else x.oldvalue
var_changes[x.fieldname] = (oldvalue, x.newvalue)
self.assertEqual(defaultmap, var_changes, "Defaults not set properly")
<｜fim▁end｜> | """
Test comparisson of dictionaries extracted from git blobs
"""
changesmap = { "foo-2" : ("2", "8"), "bar" : ("","4"), "bar-2" : ("","5")}
self.commit_vars(to_add = { "foo" : "1", "foo-2" : "2", "foo-3" : "3" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "foo-2" : "8", "bar" : "4", "bar-2" : "5" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = { x.fieldname : (x.oldvalue, x.newvalue) for x in change_records}
self.assertEqual(changesmap, var_changes, "Changes not reported correctly") |
<|file_name|>buildhistory.py<|end_file_name|><｜fim▁begin｜>import os
import unittest
import tempfile
from git import Repo
from oeqa.utils.commands import get_bb_var
from oe.buildhistory_analysis import blob_to_dict, compare_dict_blobs
class TestBlobParsing(unittest.TestCase):
def setUp(self):
import time
self.repo_path = tempfile.mkdtemp(prefix='selftest-buildhistory',
dir=get_bb_var('TOPDIR'))
self.repo = Repo.init(self.repo_path)
self.test_file = "test"
self.var_map = {}
def tearDown(self):
import shutil
shutil.rmtree(self.repo_path)
def commit_vars(self, to_add={}, to_remove = [], msg="A commit message"):
if len(to_add) == 0 and len(to_remove) == 0:
return
for k in to_remove:
self.var_map.pop(x,None)
for k in to_add:
self.var_map[k] = to_add[k]
with open(os.path.join(self.repo_path, self.test_file), 'w') as repo_file:
for k in self.var_map:
repo_file.write("%s = %s\n" % (k, self.var_map[k]))
self.repo.git.add("--all")
self.repo.git.commit(message=msg)
def test_blob_to_dict(self):
"""
Test convertion of git blobs to dictionary
"""
valuesmap = { "foo" : "1", "bar" : "2" }
self.commit_vars(to_add = valuesmap)
blob = self.repo.head.commit.tree.blobs[0]
self.assertEqual(valuesmap, blob_to_dict(blob),
"commit was not translated correctly to dictionary")
def test_compare_dict_blobs(self):
"""
Test comparisson of dictionaries extracted from git blobs
"""
changesmap = { "foo-2" : ("2", "8"), "bar" : ("","4"), "bar-2" : ("","5")}
self.commit_vars(to_add = { "foo" : "1", "foo-2" : "2", "foo-3" : "3" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "foo-2" : "8", "bar" : "4", "bar-2" : "5" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = { x.fieldname : (x.oldvalue, x.newvalue) for x in change_records}
self.assertEqual(changesmap, var_changes, "Changes not reported correctly")
def test_compare_dict_blobs_default(self):
<|fim_middle|>
<｜fim▁end｜> | """
Test default values for comparisson of git blob dictionaries
"""
defaultmap = { x : ("default", "1") for x in ["PKG", "PKGE", "PKGV", "PKGR"]}
self.commit_vars(to_add = { "foo" : "1" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "PKG" : "1", "PKGE" : "1", "PKGV" : "1", "PKGR" : "1" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = {}
for x in change_records:
oldvalue = "default" if ("default" in x.oldvalue) else x.oldvalue
var_changes[x.fieldname] = (oldvalue, x.newvalue)
self.assertEqual(defaultmap, var_changes, "Defaults not set properly") |
<|file_name|>buildhistory.py<|end_file_name|><｜fim▁begin｜>import os
import unittest
import tempfile
from git import Repo
from oeqa.utils.commands import get_bb_var
from oe.buildhistory_analysis import blob_to_dict, compare_dict_blobs
class TestBlobParsing(unittest.TestCase):
def setUp(self):
import time
self.repo_path = tempfile.mkdtemp(prefix='selftest-buildhistory',
dir=get_bb_var('TOPDIR'))
self.repo = Repo.init(self.repo_path)
self.test_file = "test"
self.var_map = {}
def tearDown(self):
import shutil
shutil.rmtree(self.repo_path)
def commit_vars(self, to_add={}, to_remove = [], msg="A commit message"):
if len(to_add) == 0 and len(to_remove) == 0:
<|fim_middle|>
for k in to_remove:
self.var_map.pop(x,None)
for k in to_add:
self.var_map[k] = to_add[k]
with open(os.path.join(self.repo_path, self.test_file), 'w') as repo_file:
for k in self.var_map:
repo_file.write("%s = %s\n" % (k, self.var_map[k]))
self.repo.git.add("--all")
self.repo.git.commit(message=msg)
def test_blob_to_dict(self):
"""
Test convertion of git blobs to dictionary
"""
valuesmap = { "foo" : "1", "bar" : "2" }
self.commit_vars(to_add = valuesmap)
blob = self.repo.head.commit.tree.blobs[0]
self.assertEqual(valuesmap, blob_to_dict(blob),
"commit was not translated correctly to dictionary")
def test_compare_dict_blobs(self):
"""
Test comparisson of dictionaries extracted from git blobs
"""
changesmap = { "foo-2" : ("2", "8"), "bar" : ("","4"), "bar-2" : ("","5")}
self.commit_vars(to_add = { "foo" : "1", "foo-2" : "2", "foo-3" : "3" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "foo-2" : "8", "bar" : "4", "bar-2" : "5" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = { x.fieldname : (x.oldvalue, x.newvalue) for x in change_records}
self.assertEqual(changesmap, var_changes, "Changes not reported correctly")
def test_compare_dict_blobs_default(self):
"""
Test default values for comparisson of git blob dictionaries
"""
defaultmap = { x : ("default", "1") for x in ["PKG", "PKGE", "PKGV", "PKGR"]}
self.commit_vars(to_add = { "foo" : "1" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "PKG" : "1", "PKGE" : "1", "PKGV" : "1", "PKGR" : "1" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = {}
for x in change_records:
oldvalue = "default" if ("default" in x.oldvalue) else x.oldvalue
var_changes[x.fieldname] = (oldvalue, x.newvalue)
self.assertEqual(defaultmap, var_changes, "Defaults not set properly")
<｜fim▁end｜> | return |
<|file_name|>buildhistory.py<|end_file_name|><｜fim▁begin｜>import os
import unittest
import tempfile
from git import Repo
from oeqa.utils.commands import get_bb_var
from oe.buildhistory_analysis import blob_to_dict, compare_dict_blobs
class TestBlobParsing(unittest.TestCase):
def <|fim_middle|>(self):
import time
self.repo_path = tempfile.mkdtemp(prefix='selftest-buildhistory',
dir=get_bb_var('TOPDIR'))
self.repo = Repo.init(self.repo_path)
self.test_file = "test"
self.var_map = {}
def tearDown(self):
import shutil
shutil.rmtree(self.repo_path)
def commit_vars(self, to_add={}, to_remove = [], msg="A commit message"):
if len(to_add) == 0 and len(to_remove) == 0:
return
for k in to_remove:
self.var_map.pop(x,None)
for k in to_add:
self.var_map[k] = to_add[k]
with open(os.path.join(self.repo_path, self.test_file), 'w') as repo_file:
for k in self.var_map:
repo_file.write("%s = %s\n" % (k, self.var_map[k]))
self.repo.git.add("--all")
self.repo.git.commit(message=msg)
def test_blob_to_dict(self):
"""
Test convertion of git blobs to dictionary
"""
valuesmap = { "foo" : "1", "bar" : "2" }
self.commit_vars(to_add = valuesmap)
blob = self.repo.head.commit.tree.blobs[0]
self.assertEqual(valuesmap, blob_to_dict(blob),
"commit was not translated correctly to dictionary")
def test_compare_dict_blobs(self):
"""
Test comparisson of dictionaries extracted from git blobs
"""
changesmap = { "foo-2" : ("2", "8"), "bar" : ("","4"), "bar-2" : ("","5")}
self.commit_vars(to_add = { "foo" : "1", "foo-2" : "2", "foo-3" : "3" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "foo-2" : "8", "bar" : "4", "bar-2" : "5" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = { x.fieldname : (x.oldvalue, x.newvalue) for x in change_records}
self.assertEqual(changesmap, var_changes, "Changes not reported correctly")
def test_compare_dict_blobs_default(self):
"""
Test default values for comparisson of git blob dictionaries
"""
defaultmap = { x : ("default", "1") for x in ["PKG", "PKGE", "PKGV", "PKGR"]}
self.commit_vars(to_add = { "foo" : "1" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "PKG" : "1", "PKGE" : "1", "PKGV" : "1", "PKGR" : "1" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = {}
for x in change_records:
oldvalue = "default" if ("default" in x.oldvalue) else x.oldvalue
var_changes[x.fieldname] = (oldvalue, x.newvalue)
self.assertEqual(defaultmap, var_changes, "Defaults not set properly")
<｜fim▁end｜> | setUp |
<|file_name|>buildhistory.py<|end_file_name|><｜fim▁begin｜>import os
import unittest
import tempfile
from git import Repo
from oeqa.utils.commands import get_bb_var
from oe.buildhistory_analysis import blob_to_dict, compare_dict_blobs
class TestBlobParsing(unittest.TestCase):
def setUp(self):
import time
self.repo_path = tempfile.mkdtemp(prefix='selftest-buildhistory',
dir=get_bb_var('TOPDIR'))
self.repo = Repo.init(self.repo_path)
self.test_file = "test"
self.var_map = {}
def <|fim_middle|>(self):
import shutil
shutil.rmtree(self.repo_path)
def commit_vars(self, to_add={}, to_remove = [], msg="A commit message"):
if len(to_add) == 0 and len(to_remove) == 0:
return
for k in to_remove:
self.var_map.pop(x,None)
for k in to_add:
self.var_map[k] = to_add[k]
with open(os.path.join(self.repo_path, self.test_file), 'w') as repo_file:
for k in self.var_map:
repo_file.write("%s = %s\n" % (k, self.var_map[k]))
self.repo.git.add("--all")
self.repo.git.commit(message=msg)
def test_blob_to_dict(self):
"""
Test convertion of git blobs to dictionary
"""
valuesmap = { "foo" : "1", "bar" : "2" }
self.commit_vars(to_add = valuesmap)
blob = self.repo.head.commit.tree.blobs[0]
self.assertEqual(valuesmap, blob_to_dict(blob),
"commit was not translated correctly to dictionary")
def test_compare_dict_blobs(self):
"""
Test comparisson of dictionaries extracted from git blobs
"""
changesmap = { "foo-2" : ("2", "8"), "bar" : ("","4"), "bar-2" : ("","5")}
self.commit_vars(to_add = { "foo" : "1", "foo-2" : "2", "foo-3" : "3" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "foo-2" : "8", "bar" : "4", "bar-2" : "5" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = { x.fieldname : (x.oldvalue, x.newvalue) for x in change_records}
self.assertEqual(changesmap, var_changes, "Changes not reported correctly")
def test_compare_dict_blobs_default(self):
"""
Test default values for comparisson of git blob dictionaries
"""
defaultmap = { x : ("default", "1") for x in ["PKG", "PKGE", "PKGV", "PKGR"]}
self.commit_vars(to_add = { "foo" : "1" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "PKG" : "1", "PKGE" : "1", "PKGV" : "1", "PKGR" : "1" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = {}
for x in change_records:
oldvalue = "default" if ("default" in x.oldvalue) else x.oldvalue
var_changes[x.fieldname] = (oldvalue, x.newvalue)
self.assertEqual(defaultmap, var_changes, "Defaults not set properly")
<｜fim▁end｜> | tearDown |
<|file_name|>buildhistory.py<|end_file_name|><｜fim▁begin｜>import os
import unittest
import tempfile
from git import Repo
from oeqa.utils.commands import get_bb_var
from oe.buildhistory_analysis import blob_to_dict, compare_dict_blobs
class TestBlobParsing(unittest.TestCase):
def setUp(self):
import time
self.repo_path = tempfile.mkdtemp(prefix='selftest-buildhistory',
dir=get_bb_var('TOPDIR'))
self.repo = Repo.init(self.repo_path)
self.test_file = "test"
self.var_map = {}
def tearDown(self):
import shutil
shutil.rmtree(self.repo_path)
def <|fim_middle|>(self, to_add={}, to_remove = [], msg="A commit message"):
if len(to_add) == 0 and len(to_remove) == 0:
return
for k in to_remove:
self.var_map.pop(x,None)
for k in to_add:
self.var_map[k] = to_add[k]
with open(os.path.join(self.repo_path, self.test_file), 'w') as repo_file:
for k in self.var_map:
repo_file.write("%s = %s\n" % (k, self.var_map[k]))
self.repo.git.add("--all")
self.repo.git.commit(message=msg)
def test_blob_to_dict(self):
"""
Test convertion of git blobs to dictionary
"""
valuesmap = { "foo" : "1", "bar" : "2" }
self.commit_vars(to_add = valuesmap)
blob = self.repo.head.commit.tree.blobs[0]
self.assertEqual(valuesmap, blob_to_dict(blob),
"commit was not translated correctly to dictionary")
def test_compare_dict_blobs(self):
"""
Test comparisson of dictionaries extracted from git blobs
"""
changesmap = { "foo-2" : ("2", "8"), "bar" : ("","4"), "bar-2" : ("","5")}
self.commit_vars(to_add = { "foo" : "1", "foo-2" : "2", "foo-3" : "3" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "foo-2" : "8", "bar" : "4", "bar-2" : "5" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = { x.fieldname : (x.oldvalue, x.newvalue) for x in change_records}
self.assertEqual(changesmap, var_changes, "Changes not reported correctly")
def test_compare_dict_blobs_default(self):
"""
Test default values for comparisson of git blob dictionaries
"""
defaultmap = { x : ("default", "1") for x in ["PKG", "PKGE", "PKGV", "PKGR"]}
self.commit_vars(to_add = { "foo" : "1" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "PKG" : "1", "PKGE" : "1", "PKGV" : "1", "PKGR" : "1" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = {}
for x in change_records:
oldvalue = "default" if ("default" in x.oldvalue) else x.oldvalue
var_changes[x.fieldname] = (oldvalue, x.newvalue)
self.assertEqual(defaultmap, var_changes, "Defaults not set properly")
<｜fim▁end｜> | commit_vars |
<|file_name|>buildhistory.py<|end_file_name|><｜fim▁begin｜>import os
import unittest
import tempfile
from git import Repo
from oeqa.utils.commands import get_bb_var
from oe.buildhistory_analysis import blob_to_dict, compare_dict_blobs
class TestBlobParsing(unittest.TestCase):
def setUp(self):
import time
self.repo_path = tempfile.mkdtemp(prefix='selftest-buildhistory',
dir=get_bb_var('TOPDIR'))
self.repo = Repo.init(self.repo_path)
self.test_file = "test"
self.var_map = {}
def tearDown(self):
import shutil
shutil.rmtree(self.repo_path)
def commit_vars(self, to_add={}, to_remove = [], msg="A commit message"):
if len(to_add) == 0 and len(to_remove) == 0:
return
for k in to_remove:
self.var_map.pop(x,None)
for k in to_add:
self.var_map[k] = to_add[k]
with open(os.path.join(self.repo_path, self.test_file), 'w') as repo_file:
for k in self.var_map:
repo_file.write("%s = %s\n" % (k, self.var_map[k]))
self.repo.git.add("--all")
self.repo.git.commit(message=msg)
def <|fim_middle|>(self):
"""
Test convertion of git blobs to dictionary
"""
valuesmap = { "foo" : "1", "bar" : "2" }
self.commit_vars(to_add = valuesmap)
blob = self.repo.head.commit.tree.blobs[0]
self.assertEqual(valuesmap, blob_to_dict(blob),
"commit was not translated correctly to dictionary")
def test_compare_dict_blobs(self):
"""
Test comparisson of dictionaries extracted from git blobs
"""
changesmap = { "foo-2" : ("2", "8"), "bar" : ("","4"), "bar-2" : ("","5")}
self.commit_vars(to_add = { "foo" : "1", "foo-2" : "2", "foo-3" : "3" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "foo-2" : "8", "bar" : "4", "bar-2" : "5" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = { x.fieldname : (x.oldvalue, x.newvalue) for x in change_records}
self.assertEqual(changesmap, var_changes, "Changes not reported correctly")
def test_compare_dict_blobs_default(self):
"""
Test default values for comparisson of git blob dictionaries
"""
defaultmap = { x : ("default", "1") for x in ["PKG", "PKGE", "PKGV", "PKGR"]}
self.commit_vars(to_add = { "foo" : "1" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "PKG" : "1", "PKGE" : "1", "PKGV" : "1", "PKGR" : "1" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = {}
for x in change_records:
oldvalue = "default" if ("default" in x.oldvalue) else x.oldvalue
var_changes[x.fieldname] = (oldvalue, x.newvalue)
self.assertEqual(defaultmap, var_changes, "Defaults not set properly")
<｜fim▁end｜> | test_blob_to_dict |
<|file_name|>buildhistory.py<|end_file_name|><｜fim▁begin｜>import os
import unittest
import tempfile
from git import Repo
from oeqa.utils.commands import get_bb_var
from oe.buildhistory_analysis import blob_to_dict, compare_dict_blobs
class TestBlobParsing(unittest.TestCase):
def setUp(self):
import time
self.repo_path = tempfile.mkdtemp(prefix='selftest-buildhistory',
dir=get_bb_var('TOPDIR'))
self.repo = Repo.init(self.repo_path)
self.test_file = "test"
self.var_map = {}
def tearDown(self):
import shutil
shutil.rmtree(self.repo_path)
def commit_vars(self, to_add={}, to_remove = [], msg="A commit message"):
if len(to_add) == 0 and len(to_remove) == 0:
return
for k in to_remove:
self.var_map.pop(x,None)
for k in to_add:
self.var_map[k] = to_add[k]
with open(os.path.join(self.repo_path, self.test_file), 'w') as repo_file:
for k in self.var_map:
repo_file.write("%s = %s\n" % (k, self.var_map[k]))
self.repo.git.add("--all")
self.repo.git.commit(message=msg)
def test_blob_to_dict(self):
"""
Test convertion of git blobs to dictionary
"""
valuesmap = { "foo" : "1", "bar" : "2" }
self.commit_vars(to_add = valuesmap)
blob = self.repo.head.commit.tree.blobs[0]
self.assertEqual(valuesmap, blob_to_dict(blob),
"commit was not translated correctly to dictionary")
def <|fim_middle|>(self):
"""
Test comparisson of dictionaries extracted from git blobs
"""
changesmap = { "foo-2" : ("2", "8"), "bar" : ("","4"), "bar-2" : ("","5")}
self.commit_vars(to_add = { "foo" : "1", "foo-2" : "2", "foo-3" : "3" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "foo-2" : "8", "bar" : "4", "bar-2" : "5" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = { x.fieldname : (x.oldvalue, x.newvalue) for x in change_records}
self.assertEqual(changesmap, var_changes, "Changes not reported correctly")
def test_compare_dict_blobs_default(self):
"""
Test default values for comparisson of git blob dictionaries
"""
defaultmap = { x : ("default", "1") for x in ["PKG", "PKGE", "PKGV", "PKGR"]}
self.commit_vars(to_add = { "foo" : "1" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "PKG" : "1", "PKGE" : "1", "PKGV" : "1", "PKGR" : "1" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = {}
for x in change_records:
oldvalue = "default" if ("default" in x.oldvalue) else x.oldvalue
var_changes[x.fieldname] = (oldvalue, x.newvalue)
self.assertEqual(defaultmap, var_changes, "Defaults not set properly")
<｜fim▁end｜> | test_compare_dict_blobs |
<|file_name|>buildhistory.py<|end_file_name|><｜fim▁begin｜>import os
import unittest
import tempfile
from git import Repo
from oeqa.utils.commands import get_bb_var
from oe.buildhistory_analysis import blob_to_dict, compare_dict_blobs
class TestBlobParsing(unittest.TestCase):
def setUp(self):
import time
self.repo_path = tempfile.mkdtemp(prefix='selftest-buildhistory',
dir=get_bb_var('TOPDIR'))
self.repo = Repo.init(self.repo_path)
self.test_file = "test"
self.var_map = {}
def tearDown(self):
import shutil
shutil.rmtree(self.repo_path)
def commit_vars(self, to_add={}, to_remove = [], msg="A commit message"):
if len(to_add) == 0 and len(to_remove) == 0:
return
for k in to_remove:
self.var_map.pop(x,None)
for k in to_add:
self.var_map[k] = to_add[k]
with open(os.path.join(self.repo_path, self.test_file), 'w') as repo_file:
for k in self.var_map:
repo_file.write("%s = %s\n" % (k, self.var_map[k]))
self.repo.git.add("--all")
self.repo.git.commit(message=msg)
def test_blob_to_dict(self):
"""
Test convertion of git blobs to dictionary
"""
valuesmap = { "foo" : "1", "bar" : "2" }
self.commit_vars(to_add = valuesmap)
blob = self.repo.head.commit.tree.blobs[0]
self.assertEqual(valuesmap, blob_to_dict(blob),
"commit was not translated correctly to dictionary")
def test_compare_dict_blobs(self):
"""
Test comparisson of dictionaries extracted from git blobs
"""
changesmap = { "foo-2" : ("2", "8"), "bar" : ("","4"), "bar-2" : ("","5")}
self.commit_vars(to_add = { "foo" : "1", "foo-2" : "2", "foo-3" : "3" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "foo-2" : "8", "bar" : "4", "bar-2" : "5" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = { x.fieldname : (x.oldvalue, x.newvalue) for x in change_records}
self.assertEqual(changesmap, var_changes, "Changes not reported correctly")
def <|fim_middle|>(self):
"""
Test default values for comparisson of git blob dictionaries
"""
defaultmap = { x : ("default", "1") for x in ["PKG", "PKGE", "PKGV", "PKGR"]}
self.commit_vars(to_add = { "foo" : "1" })
blob1 = self.repo.heads.master.commit.tree.blobs[0]
self.commit_vars(to_add = { "PKG" : "1", "PKGE" : "1", "PKGV" : "1", "PKGR" : "1" })
blob2 = self.repo.heads.master.commit.tree.blobs[0]
change_records = compare_dict_blobs(os.path.join(self.repo_path, self.test_file),
blob1, blob2, False, False)
var_changes = {}
for x in change_records:
oldvalue = "default" if ("default" in x.oldvalue) else x.oldvalue
var_changes[x.fieldname] = (oldvalue, x.newvalue)
self.assertEqual(defaultmap, var_changes, "Defaults not set properly")
<｜fim▁end｜> | test_compare_dict_blobs_default |
<|file_name|>wsgi.py<|end_file_name|><｜fim▁begin｜>"""
WSGI config for mongobacked project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/1.6/howto/deployment/wsgi/
"""
import os
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "mongobacked.settings")
<｜fim▁hole｜>application = get_wsgi_application()<｜fim▁end｜> | from django.core.wsgi import get_wsgi_application |
<|file_name|>282 Expression Add Operators.py<|end_file_name|><｜fim▁begin｜>"""
Given a string that contains only digits 0-9 and a target value, return all possibilities to add binary operators (not
unary) +, -, or * between the digits so they evaluate to the target value.
Examples:
"123", 6 -> ["1+2+3", "1*2*3"]<｜fim▁hole｜>"232", 8 -> ["2*3+2", "2+3*2"]
"105", 5 -> ["1*0+5","10-5"]
"00", 0 -> ["0+0", "0-0", "0*0"]
"3456237490", 9191 -> []
"""
__author__ = 'Daniel'
class Solution(object):
def addOperators(self, num, target):
"""
Adapted from https://leetcode.com/discuss/58614/java-standard-backtrace-ac-solutoin-short-and-clear
Algorithm:
1. DFS
2. Special handling for multiplication
3. Detect invalid number with leading 0's
:type num: str
:type target: int
:rtype: List[str]
"""
ret = []
self.dfs(num, target, 0, "", 0, 0, ret)
return ret
def dfs(self, num, target, pos, cur_str, cur_val, mul, ret):
if pos >= len(num):
if cur_val == target:
ret.append(cur_str)
else:
for i in xrange(pos, len(num)):
if i != pos and num[pos] == "0":
continue
nxt_val = int(num[pos:i+1])
if not cur_str:
self.dfs(num, target, i+1, "%d"%nxt_val, nxt_val, nxt_val, ret)
else:
self.dfs(num, target, i+1, cur_str+"+%d"%nxt_val, cur_val+nxt_val, nxt_val, ret)
self.dfs(num, target, i+1, cur_str+"-%d"%nxt_val, cur_val-nxt_val, -nxt_val, ret)
self.dfs(num, target, i+1, cur_str+"*%d"%nxt_val, cur_val-mul+mul*nxt_val, mul*nxt_val, ret)
if __name__ == "__main__":
assert Solution().addOperators("232", 8) == ["2+3*2", "2*3+2"]<｜fim▁end｜> | |
<|file_name|>282 Expression Add Operators.py<|end_file_name|><｜fim▁begin｜>"""
Given a string that contains only digits 0-9 and a target value, return all possibilities to add binary operators (not
unary) +, -, or * between the digits so they evaluate to the target value.
Examples:
"123", 6 -> ["1+2+3", "1*2*3"]
"232", 8 -> ["2*3+2", "2+3*2"]
"105", 5 -> ["1*0+5","10-5"]
"00", 0 -> ["0+0", "0-0", "0*0"]
"3456237490", 9191 -> []
"""
__author__ = 'Daniel'
class Solution(object):
<|fim_middle|>
if __name__ == "__main__":
assert Solution().addOperators("232", 8) == ["2+3*2", "2*3+2"]
<｜fim▁end｜> | def addOperators(self, num, target):
"""
Adapted from https://leetcode.com/discuss/58614/java-standard-backtrace-ac-solutoin-short-and-clear
Algorithm:
1. DFS
2. Special handling for multiplication
3. Detect invalid number with leading 0's
:type num: str
:type target: int
:rtype: List[str]
"""
ret = []
self.dfs(num, target, 0, "", 0, 0, ret)
return ret
def dfs(self, num, target, pos, cur_str, cur_val, mul, ret):
if pos >= len(num):
if cur_val == target:
ret.append(cur_str)
else:
for i in xrange(pos, len(num)):
if i != pos and num[pos] == "0":
continue
nxt_val = int(num[pos:i+1])
if not cur_str:
self.dfs(num, target, i+1, "%d"%nxt_val, nxt_val, nxt_val, ret)
else:
self.dfs(num, target, i+1, cur_str+"+%d"%nxt_val, cur_val+nxt_val, nxt_val, ret)
self.dfs(num, target, i+1, cur_str+"-%d"%nxt_val, cur_val-nxt_val, -nxt_val, ret)
self.dfs(num, target, i+1, cur_str+"*%d"%nxt_val, cur_val-mul+mul*nxt_val, mul*nxt_val, ret) |
<|file_name|>282 Expression Add Operators.py<|end_file_name|><｜fim▁begin｜>"""
Given a string that contains only digits 0-9 and a target value, return all possibilities to add binary operators (not
unary) +, -, or * between the digits so they evaluate to the target value.
Examples:
"123", 6 -> ["1+2+3", "1*2*3"]
"232", 8 -> ["2*3+2", "2+3*2"]
"105", 5 -> ["1*0+5","10-5"]
"00", 0 -> ["0+0", "0-0", "0*0"]
"3456237490", 9191 -> []
"""
__author__ = 'Daniel'
class Solution(object):
def addOperators(self, num, target):
<|fim_middle|>
def dfs(self, num, target, pos, cur_str, cur_val, mul, ret):
if pos >= len(num):
if cur_val == target:
ret.append(cur_str)
else:
for i in xrange(pos, len(num)):
if i != pos and num[pos] == "0":
continue
nxt_val = int(num[pos:i+1])
if not cur_str:
self.dfs(num, target, i+1, "%d"%nxt_val, nxt_val, nxt_val, ret)
else:
self.dfs(num, target, i+1, cur_str+"+%d"%nxt_val, cur_val+nxt_val, nxt_val, ret)
self.dfs(num, target, i+1, cur_str+"-%d"%nxt_val, cur_val-nxt_val, -nxt_val, ret)
self.dfs(num, target, i+1, cur_str+"*%d"%nxt_val, cur_val-mul+mul*nxt_val, mul*nxt_val, ret)
if __name__ == "__main__":
assert Solution().addOperators("232", 8) == ["2+3*2", "2*3+2"]
<｜fim▁end｜> | """
Adapted from https://leetcode.com/discuss/58614/java-standard-backtrace-ac-solutoin-short-and-clear
Algorithm:
1. DFS
2. Special handling for multiplication
3. Detect invalid number with leading 0's
:type num: str
:type target: int
:rtype: List[str]
"""
ret = []
self.dfs(num, target, 0, "", 0, 0, ret)
return ret |
<|file_name|>282 Expression Add Operators.py<|end_file_name|><｜fim▁begin｜>"""
Given a string that contains only digits 0-9 and a target value, return all possibilities to add binary operators (not
unary) +, -, or * between the digits so they evaluate to the target value.
Examples:
"123", 6 -> ["1+2+3", "1*2*3"]
"232", 8 -> ["2*3+2", "2+3*2"]
"105", 5 -> ["1*0+5","10-5"]
"00", 0 -> ["0+0", "0-0", "0*0"]
"3456237490", 9191 -> []
"""
__author__ = 'Daniel'
class Solution(object):
def addOperators(self, num, target):
"""
Adapted from https://leetcode.com/discuss/58614/java-standard-backtrace-ac-solutoin-short-and-clear
Algorithm:
1. DFS
2. Special handling for multiplication
3. Detect invalid number with leading 0's
:type num: str
:type target: int
:rtype: List[str]
"""
ret = []
self.dfs(num, target, 0, "", 0, 0, ret)
return ret
def dfs(self, num, target, pos, cur_str, cur_val, mul, ret):
<|fim_middle|>
if __name__ == "__main__":
assert Solution().addOperators("232", 8) == ["2+3*2", "2*3+2"]
<｜fim▁end｜> | if pos >= len(num):
if cur_val == target:
ret.append(cur_str)
else:
for i in xrange(pos, len(num)):
if i != pos and num[pos] == "0":
continue
nxt_val = int(num[pos:i+1])
if not cur_str:
self.dfs(num, target, i+1, "%d"%nxt_val, nxt_val, nxt_val, ret)
else:
self.dfs(num, target, i+1, cur_str+"+%d"%nxt_val, cur_val+nxt_val, nxt_val, ret)
self.dfs(num, target, i+1, cur_str+"-%d"%nxt_val, cur_val-nxt_val, -nxt_val, ret)
self.dfs(num, target, i+1, cur_str+"*%d"%nxt_val, cur_val-mul+mul*nxt_val, mul*nxt_val, ret) |
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